This code conducts the data prep and analysis for a within race/ethnicity analysis for depression (CBCL - Depressin Subscale), obesity risk (Waist Circumference to Height Ratio [WC/HT]), and relative pubertal timing (Pubertal Development Scale) in ABCD.
##rm(list = ls())
library(psych)
#Load subject and family identifiers
Identifiers <- read.csv("C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Data/abcd-data-release-5.1/core/abcd-general/abcd_y_lt.csv", header = TRUE, sep = ",", dec = ".")
#View(identifiers)
#print variable names and labels
#print(Identifiers[1,])
#Create key for Identifiers:
Identifiers.varlabels <- Identifiers[1,]
#Remove first row of Identifiers so we are left with only the data:
Identifiers.data <- Identifiers[-1,]
# Rename/recode variables
Identifiers.data$IID <- Identifiers.data[,"src_subject_id"]
Identifiers.data$FID <- Identifiers.data[,"rel_family_id"]
Identifiers.data$BID <- Identifiers.data[,"rel_birth_id"]
#dim(Identifiers.data)
Identifiers.data <- Identifiers.data[order(Identifiers.data$IID,Identifiers.data$FID,Identifiers.data$BID),]
#View(Identifiers.data)
Baseline.Identifiers<-Identifiers.data[Identifiers.data$eventname=="baseline_year_1_arm_1",]
Y1.Identifiers<-Identifiers.data[Identifiers.data$eventname=="1_year_follow_up_y_arm_1",]
Y2.Identifiers<-Identifiers.data[Identifiers.data$eventname=="2_year_follow_up_y_arm_1",]
Y3.Identifiers<-Identifiers.data[Identifiers.data$eventname=="3_year_follow_up_y_arm_1",]
#print(names(Identifiers.data))
#table(Identifiers.data$eventname)
#summary(Y1.data$interview_age)
# Load demographic data
demo <- read.csv("C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Data/abcd-data-release-5.1/core/abcd-general/abcd_p_demo.csv", header = TRUE, sep = ",", dec= ".")
# View(identifiers)
# Print variable names and labels
#print(demo[1,])
# Create key for Identifiers:
demo.varlabels <- demo[1,]
# Remove first row of Identifiers so we are left with only the data:
demo.data <- demo[-1,]
# Rename subject ID
demo.data$IID <- demo.data[,"src_subject_id"]
# Baseline
Baseline.demo<-demo.data[demo.data$eventname=="baseline_year_1_arm_1",]
colnames(Baseline.demo)[colnames(Baseline.demo) == "demo_sex_v2"] <- "sex"
# Create binary variables for each race/ethnicity
# 1 = White; 2 = Black; 3 = Hispanic; 4 = Asian; 5 = Other
freq_table <- table(Baseline.demo$race_ethnicity)
print(freq_table)
##
## 1 2 3 4 5
## 6172 1784 2410 252 1247
# Create binary variables for each race/ethnicity
Baseline.demo$White <- ifelse(Baseline.demo$race_ethnicity == 1, 1, 0)
Baseline.demo$Black <- ifelse(Baseline.demo$race_ethnicity == 2, 1, 0)
Baseline.demo$Hispanic <- ifelse(Baseline.demo$race_ethnicity == 3, 1, 0)
Baseline.demo$Asian <- ifelse(Baseline.demo$race_ethnicity == 4, 1, 0)
Baseline.demo$Other <- ifelse(Baseline.demo$race_ethnicity == 5, 1, 0)
# Frequency Tables for race/ethnicity
freq_White <- table(Baseline.demo$White)
print(freq_White)
##
## 0 1
## 5693 6172
freq_Black <- table(Baseline.demo$Black)
print(freq_Black)
##
## 0 1
## 10081 1784
freq_Hispanic <- table(Baseline.demo$Hispanic)
print(freq_Hispanic)
##
## 0 1
## 9455 2410
freq_Asian <- table(Baseline.demo$Asian)
print(freq_Asian)
##
## 0 1
## 11613 252
freq_Other <- table(Baseline.demo$Other)
print(freq_Other)
##
## 0 1
## 10618 1247
# Subset dataset for White, Black, Hispanic
Baseline.demo <- subset(Baseline.demo, White == 1 | Black == 1 | Hispanic == 1)
# Create indicator var for subset analyses
Baseline.demo$race3 <- ifelse(Baseline.demo$White == 1, 1,
ifelse(Baseline.demo$Black == 1, 2,
ifelse(Baseline.demo$Hispanic == 1, 3, NA)))
freq_race3 <- table(Baseline.demo$race3)
#print(freq_race3)
# Subset so later data sets are smaller
Baseline.demo <- Baseline.demo[, c("IID", "sex", "race_ethnicity", "White",
"Black", "Hispanic", "Asian", "Other", "race3",
"demo_prnt_ed_v2_2yr_l", # Respondent
"demo_prtnr_ed_v2_2yr_l", # Partner
"demo_comb_income_v2",
"demo_prnt_age_v2")]
#Load cbcl data
cbcl <- read.csv("C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Data/abcd-data-release-5.1/core/mental-health/mh_p_cbcl.csv", header = TRUE, sep = ",", dec = ".")
#View(cbcl)
describe(cbcl$cbcl_scr_syn_withdep_r) #Internalizing CBCL Syndrome Scale (raw score)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 48727 1.24 1.96 0 0.81 0 0 16 16 2.32 6.61 0.01
summary(cbcl$cbcl_scr_syn_withdep_r)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 0.000 1.244 2.000 16.000 10
#Create key for Identifiers:
cbcl.varlabels <- cbcl[1,]
#Remove first row of Identifiers so we are left with only the data:
cbcl.data <- cbcl[-1,]
#Rename subject ID
cbcl.data$IID <- cbcl.data[,"src_subject_id"]
#Create 0, 1, 2,3 assessment data sets
Baseline.cbcl<-cbcl.data[cbcl.data$eventname=="baseline_year_1_arm_1",]
Y1.cbcl<-cbcl.data[cbcl.data$eventname=="1_year_follow_up_y_arm_1",]
Y2.cbcl<-cbcl.data[cbcl.data$eventname=="2_year_follow_up_y_arm_1",]
Y3.cbcl<-cbcl.data[cbcl.data$eventname=="3_year_follow_up_y_arm_1",]
###############################################################################
################################ Baseline (10.5)###############################
###############################################################################
# Merge Baseline.cbcl with Baseline.Identifiers based on IID to get age
Merged.Baseline_cbcl_identifiers <- merge(Baseline.cbcl, Baseline.Identifiers, by = "IID")
# Then, merge Merged.Baseline_cbcl_identifiers with baseline.demo based on IID to get sex variable
Merged.Baseline_ID_cbcl <- merge(Merged.Baseline_cbcl_identifiers, Baseline.demo, by = "IID")
############### Regress age out of depression score by sex separately #################
library(modelr)
library(tidyr)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
# Rename variables for code below
colnames(Merged.Baseline_ID_cbcl)[colnames(Merged.Baseline_ID_cbcl) == "interview_age"] <- "age"
colnames(Merged.Baseline_ID_cbcl)[colnames(Merged.Baseline_ID_cbcl) == "cbcl_scr_syn_withdep_r"] <- "int"
# Subset the data
int_raw <- Merged.Baseline_ID_cbcl[, c("IID", "age", "sex", "int" )]
# Make data set for boys and girls
boys_data <- int_raw[int_raw$sex == 1, ]
girls_data <- int_raw[int_raw$sex == 2, ]
#################### Regress internalizing scores for boys #####################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(boys_data)
# Drop cases missing on int and age
boys_data <- boys_data[!is.na(boys_data$age) & !is.na(boys_data$int), ]
# Fit the linear regression model
model <- lm(int ~ age, data = boys_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into intt var
boys_data$intt <- residuals
# Display the modified data table with residuals
# print(boys_data)
######################## Regress int scores for girls ##########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(girls_data)
# Drop cases missing on int and age
girls_data <- girls_data[!is.na(girls_data$age) & !is.na(girls_data$int), ]
# Fit the linear regression model
model <- lm(int ~ age, data = girls_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into intt var
girls_data$intt <- residuals
# Display the modified data table with residuals
# print(girls_data)
# Merge Data
merged_data <- merge(boys_data, girls_data, by = "IID", all = TRUE, suffixes = c(".boys", ".girls"))
# Combine intt variables and create a new combined intt column
merged_data <- merged_data %>%
mutate(intt = coalesce(intt.boys, intt.girls))
# Display the merged data with combined intt variable
# print(merged_data)
# Subset to only IDD and intt score
merged_data <- merged_data[, c("IID", "intt")]
# Merge with raw int data
merged_int <- merge(int_raw, merged_data, by = "IID", all = TRUE)
# print(merged_int)
# Rename variables to keep in each assessment data set
library(data.table)
##
## Attaching package: 'data.table'
## The following objects are masked from 'package:dplyr':
##
## between, first, last
variables_to_keep_Baseline <- c("IID","int","intt", "age")
# Convert the data frame to a data.table
setDT(merged_int)
# Subset the data table and create a new data table
subset_data <- merged_int[, ..variables_to_keep_Baseline]
# Rename the columns by appending "_Baseline"
colnames(subset_data) <- paste0(colnames(subset_data), "_Baseline")
# Rename IID for merge
colnames(subset_data)[colnames(subset_data) == "IID_Baseline"] <- "IID"
# rename data set for merge
int_Baseline <- subset_data
###############################################################################
################################ Year 1 (11.5) ################################
###############################################################################
# Merge Baseline.cbcl with Baseline.Identifiers based on IID to get age
Merged.Y1_cbcl_identifiers <- merge(Y1.cbcl, Y1.Identifiers, by = "IID")
# Then, merge Merged.Y1_cbcl_identifiers with baseline.demo based on IID to get sex variable
Merged.Y1_ID_cbcl <- merge(Merged.Y1_cbcl_identifiers, Baseline.demo, by = "IID")
############### Regress age out of BMI score by sex separately #################
library(modelr)
library(tidyr)
library(dplyr)
# Rename variables for code below
colnames(Merged.Y1_ID_cbcl)[colnames(Merged.Y1_ID_cbcl) == "interview_age"] <- "age"
colnames(Merged.Y1_ID_cbcl)[colnames(Merged.Y1_ID_cbcl) == "cbcl_scr_syn_withdep_r"] <- "int"
# Subset the data
int_raw <- Merged.Y1_ID_cbcl[, c("IID", "age", "sex", "int" )]
# Make data set for boys and girls
boys_data <- int_raw[int_raw$sex == 1, ]
girls_data <- int_raw[int_raw$sex == 2, ]
#################### Regress internalizing scores for boys #####################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(boys_data)
# Drop cases missing on int and age
boys_data <- boys_data[!is.na(boys_data$age) & !is.na(boys_data$int), ]
# Fit the linear regression model
model <- lm(int ~ age, data = boys_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into intt var
boys_data$intt <- residuals
# Display the modified data table with residuals
# print(boys_data)
######################## Regress int scores for girls ##########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(girls_data)
# Drop cases missing on int and age
girls_data <- girls_data[!is.na(girls_data$age) & !is.na(girls_data$int), ]
# Fit the linear regression model
model <- lm(int ~ age, data = girls_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into intt var
girls_data$intt <- residuals
# Display the modified data table with residuals
# print(girls_data)
# Merge Data
merged_data <- merge(boys_data, girls_data, by = "IID", all = TRUE, suffixes = c(".boys", ".girls"))
# Combine intt variables and create a new combined intt column
merged_data <- merged_data %>%
mutate(intt = coalesce(intt.boys, intt.girls))
# Display the merged data with combined intt variable
# print(merged_data)
# Subset to only IDD and intt score
merged_data <- merged_data[, c("IID", "intt")]
# Merge with raw int data
merged_int <- merge(int_raw, merged_data, by = "IID", all = TRUE)
# print(merged_int)
# Rename variables to keep in each assessment data set
library(data.table)
variables_to_keep_Y1 <- c("IID","int","intt", "age")
# Convert the data frame to a data.table
setDT(merged_int)
# Subset the data table and create a new data table
subset_data <- merged_int[, ..variables_to_keep_Y1]
# Rename the columns by appending "_Y1"
colnames(subset_data) <- paste0(colnames(subset_data), "_Y1")
# Rename IID for merge
colnames(subset_data)[colnames(subset_data) == "IID_Y1"] <- "IID"
# rename data set for merge
int_Y1 <- subset_data
###############################################################################
################################ Year 2 (12.5) ################################
###############################################################################
# Merge Baseline.cbcl with Baseline.Identifiers based on IID to get age
Merged.Y2_cbcl_identifiers <- merge(Y2.cbcl, Y2.Identifiers, by = "IID")
# Then, merge Merged.Y2_cbcl_identifiers with baseline.demo based on IID to get sex variable
Merged.Y2_ID_cbcl <- merge(Merged.Y2_cbcl_identifiers, Baseline.demo, by = "IID")
############### Regress age out of BMI score by sex separately #################
library(modelr)
library(tidyr)
library(dplyr)
# Rename variables for code below
colnames(Merged.Y2_ID_cbcl)[colnames(Merged.Y2_ID_cbcl) == "interview_age"] <- "age"
colnames(Merged.Y2_ID_cbcl)[colnames(Merged.Y2_ID_cbcl) == "cbcl_scr_syn_withdep_r"] <- "int"
# Subset the data
int_raw <- Merged.Y2_ID_cbcl[, c("IID", "age", "sex", "int" )]
# Make data set for boys and girls
boys_data <- int_raw[int_raw$sex == 1, ]
girls_data <- int_raw[int_raw$sex == 2, ]
#################### Regress internalizing scores for boys #####################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(boys_data)
# Drop cases missing on int and age
boys_data <- boys_data[!is.na(boys_data$age) & !is.na(boys_data$int), ]
# Fit the linear regression model
model <- lm(int ~ age, data = boys_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into intt var
boys_data$intt <- residuals
# Display the modified data table with residuals
# print(boys_data)
######################## Regress int scores for girls ##########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(girls_data)
# Drop cases missing on int and age
girls_data <- girls_data[!is.na(girls_data$age) & !is.na(girls_data$int), ]
# Fit the linear regression model
model <- lm(int ~ age, data = girls_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into intt var
girls_data$intt <- residuals
# Display the modified data table with residuals
#print(girls_data)
# Merge Data
merged_data <- merge(boys_data, girls_data, by = "IID", all = TRUE, suffixes = c(".boys", ".girls"))
# Combine intt variables and create a new combined intt column
merged_data <- merged_data %>%
mutate(intt = coalesce(intt.boys, intt.girls))
# Display the merged data with combined intt variable
# print(merged_data)
# Subset to only IDD and intt score
merged_data <- merged_data[, c("IID", "intt")]
# Merge with raw int data
merged_int <- merge(int_raw, merged_data, by = "IID", all = TRUE)
# print(merged_int)
# Rename variables to keep in each assessment data set
library(data.table)
variables_to_keep_Y2 <- c("IID","int","intt", "age")
# Convert the data frame to a data.table
setDT(merged_int)
# Subset the data table and create a new data table
subset_data <- merged_int[, ..variables_to_keep_Y2]
# Rename the columns by appending "_Y2"
colnames(subset_data) <- paste0(colnames(subset_data), "_Y2")
# Rename IID for merge
colnames(subset_data)[colnames(subset_data) == "IID_Y2"] <- "IID"
# rename data set for merge
int_Y2 <- subset_data
###############################################################################
################################ Year 3 (13.5) ################################
###############################################################################
# Merge Baseline.cbcl with Baseline.Identifiers based on IID to get age
Merged.Y3_cbcl_identifiers <- merge(Y3.cbcl, Y3.Identifiers, by = "IID")
# Then, merge Merged.Y3_cbcl_identifiers with baseline.demo based on IID to get sex variable
Merged.Y3_ID_cbcl <- merge(Merged.Y3_cbcl_identifiers, Baseline.demo, by = "IID")
############### Regress age out of BMI score by sex separately #################
library(modelr)
library(tidyr)
library(dplyr)
# Rename variables for code below
colnames(Merged.Y3_ID_cbcl)[colnames(Merged.Y3_ID_cbcl) == "interview_age"] <- "age"
colnames(Merged.Y3_ID_cbcl)[colnames(Merged.Y3_ID_cbcl) == "cbcl_scr_syn_withdep_r"] <- "int"
# Subset the data
int_raw <- Merged.Y3_ID_cbcl[, c("IID", "age", "sex", "int" )]
# Make data set for boys and girls
boys_data <- int_raw[int_raw$sex == 1, ]
girls_data <- int_raw[int_raw$sex == 2, ]
#################### Regress internalizing scores for boys #####################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(boys_data)
# Drop cases missing on int and age
boys_data <- boys_data[!is.na(boys_data$age) & !is.na(boys_data$int), ]
# Fit the linear regression model
model <- lm(int ~ age, data = boys_data)
#summary(model)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into intt var
boys_data$intt <- residuals
# Display the modified data table with residuals
# print(boys_data)
######################## Regress int scores for girls ##########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(girls_data)
# Drop cases missing on int and age
girls_data <- girls_data[!is.na(girls_data$age) & !is.na(girls_data$int), ]
# Fit the linear regression model
model <- lm(int ~ age, data = girls_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into intt var
girls_data$intt <- residuals
# Display the modified data table with residuals
# print(girls_data)
# Merge Data
merged_data <- merge(boys_data, girls_data, by = "IID", all = TRUE, suffixes = c(".boys", ".girls"))
# Combine intt variables and create a new combined intt column
merged_data <- merged_data %>%
mutate(intt = coalesce(intt.boys, intt.girls))
# Display the merged data with combined intt variable
# print(merged_data)
# Subset to only IDD and intt score
merged_data <- merged_data[, c("IID", "intt")]
# Merge with raw int data
merged_int <- merge(int_raw, merged_data, by = "IID", all = TRUE)
# print(merged_int)
# Rename variables to keep in each assessment data set
library(data.table)
variables_to_keep_Y3 <- c("IID","int","intt", "age")
# Convert the data frame to a data.table
setDT(merged_int)
# Subset the data table and create a new data table
subset_data <- merged_int[, ..variables_to_keep_Y3]
# Rename the columns by appending "_Y3"
colnames(subset_data) <- paste0(colnames(subset_data), "_Y3")
# Rename IID for merge
colnames(subset_data)[colnames(subset_data) == "IID_Y3"] <- "IID"
# rename data set for merge
int_Y3 <- subset_data
############### Merge all internalizing data sets together ####################
# Merge all int data sets together
library(data.table)
# List of datasets to merge
int_datasets <- list(int_Baseline, int_Y1, int_Y2, int_Y3)
# Merge all data.tables using full outer join based on IID
int_merged_data <- Reduce(function(x, y) merge(x, y, by = "IID", all = TRUE), int_datasets)
# Internalizing Descriptive Statistics
library(psych)
# Select the variables you want to describe
variables_to_describe <- c("age_Baseline", "int_Baseline", "intt_Baseline",
"age_Y1", "int_Y1", "intt_Y1",
"age_Y2", "int_Y2", "intt_Y2",
"age_Y3", "int_Y3", "intt_Y3")
# Convert data.table to data frame
data_frame <- as.data.frame(int_merged_data)
# Use the describe() function to obtain descriptive statistics
variable_descriptions <- describe(data_frame[variables_to_describe])
# Print the descriptive statistics
print(variable_descriptions)
## vars n mean sd median trimmed mad min max range
## age_Baseline 1 10360 118.97 7.49 119.00 118.87 10.38 107.00 133.00 26.00
## int_Baseline 2 10358 1.03 1.70 0.00 0.64 0.00 0.00 15.00 15.00
## intt_Baseline 3 10355 0.00 1.70 -0.91 -0.37 0.34 -1.16 13.91 15.08
## age_Y1 4 9784 131.08 7.72 131.00 130.99 10.38 117.00 149.00 32.00
## int_Y1 5 9783 1.11 1.78 0.00 0.71 0.00 0.00 14.00 14.00
## intt_Y1 6 9780 0.00 1.77 -0.99 -0.38 0.36 -1.30 12.91 14.21
## age_Y2 7 9523 144.28 8.01 144.00 144.18 10.38 127.00 168.00 41.00
## int_Y2 8 9522 1.23 1.96 0.00 0.79 0.00 0.00 16.00 16.00
## intt_Y2 9 9519 0.00 1.96 -1.02 -0.42 0.63 -1.70 15.03 16.73
## age_Y3 10 8825 154.93 7.77 155.00 154.83 10.38 137.00 175.00 38.00
## int_Y3 11 8821 1.44 2.15 1.00 0.97 1.48 0.00 15.00 15.00
## intt_Y3 12 8819 0.00 2.15 -0.67 -0.44 1.18 -1.82 13.69 15.52
## skew kurtosis se
## age_Baseline 0.07 -1.26 0.07
## int_Baseline 2.52 8.16 0.02
## intt_Baseline 2.51 8.15 0.02
## age_Y1 0.07 -1.18 0.08
## int_Y1 2.42 7.28 0.02
## intt_Y1 2.42 7.26 0.02
## age_Y2 0.11 -0.93 0.08
## int_Y2 2.38 7.12 0.02
## intt_Y2 2.36 7.07 0.02
## age_Y3 0.09 -1.02 0.08
## int_Y3 2.14 5.47 0.02
## intt_Y3 2.12 5.38 0.02
library(ggplot2)
##
## Attaching package: 'ggplot2'
## The following objects are masked from 'package:psych':
##
## %+%, alpha
# Create four-panel histogram
library(ggplot2)
# Combine all int variables into a single column
all_int <- c(data_frame$int_Baseline, data_frame$int_Y1, data_frame$int_Y2, data_frame$int_Y3)
# Create a data frame with the combined int values
combined_data <- data.frame(int = all_int, Timepoint = rep(c("Baseline", "Y1", "Y2", "Y3"), each = nrow(data_frame)))
# Create the combined histogram with APA-style formatting
ggplot(combined_data, aes(x = int)) +
geom_histogram(binwidth = 1, fill = "#0072B2", color = "#0072B2", alpha = 0.7) +
labs(title = "Histogram Internalizing Depression Symptoms Across Study Timepoints", x = "int", y = "Frequency") +
facet_wrap(~ Timepoint, scales = "free_x", nrow = 2) +
theme_minimal() +
theme(panel.grid = element_blank(),
panel.border = element_rect(color = "black", fill = NA),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10),
strip.text = element_text(size = 12),
plot.title = element_text(size = 14, hjust = 0.5),
strip.background = element_rect(fill = "#E5E5E5"),
legend.position = "none")
## Warning: Removed 2972 rows containing non-finite values (`stat_bin()`).
################################################################################
# install.packages("corrplot")
library(corrplot)
## corrplot 0.92 loaded
library(dplyr)
library(ggplot2)
library(reshape2)
##
## Attaching package: 'reshape2'
## The following objects are masked from 'package:data.table':
##
## dcast, melt
## The following object is masked from 'package:tidyr':
##
## smiths
# Select the int variables
int_variables <- c("int_Baseline", "int_Y1", "int_Y2", "int_Y3")
# Subset the data for the selected int variables
int_data <- data_frame %>%
select(all_of(int_variables))
# Compute the correlation matrix
cor_matrix <- cor(int_data, use = "complete.obs")
cor_matrix
## int_Baseline int_Y1 int_Y2 int_Y3
## int_Baseline 1.0000000 0.6275352 0.5472477 0.4613667
## int_Y1 0.6275352 1.0000000 0.6263788 0.5508441
## int_Y2 0.5472477 0.6263788 1.0000000 0.6314528
## int_Y3 0.4613667 0.5508441 0.6314528 1.0000000
# Create a ggheatmap
ggheatmap <- ggplot(melt(cor_matrix), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 4) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
################################################################################
# Convert to long to examine trajectories
library(tidyr)
# Subset
long_data <- data_frame[, c("IID", "int_Baseline", "int_Y1", "int_Y2", "int_Y3")]
library(reshape2)
# Convert wide data to long format using melt()
long_data <- melt(data_frame, id.vars = "IID", measure.vars = c("int_Baseline", "int_Y1", "int_Y2", "int_Y3"),
variable.name = "Timepoint", value.name = "int")
# Sort the long-format data by IID
sorted_long_data <- long_data %>%
arrange(IID)
# Print the long-format data
# print(long_data)
library(dplyr)
library(ggplot2)
# Randomly select 1000 unique IIDs
unique_iids <- sorted_long_data %>%
distinct(IID) %>%
sample_n(200) # Select 1000 random IIDs
# Subset the data based on the selected IIDs
subset_data <- sorted_long_data %>%
filter(IID %in% unique_iids$IID)
# Create a longitudinal plot of int across timepoints
ggplot(subset_data, aes(x = Timepoint, y = int, group = IID, color = IID)) +
geom_line(size = 1) +
labs(title = "Longitudinal Plot of Internalizing Depression Symptoms Across Timepoints", x = "Timepoint", y = "int") +
theme_minimal() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(color = "black", fill = NA),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10),
plot.title = element_text(size = 14, hjust = 0.5))
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Removed 43 rows containing missing values (`geom_line()`).
#Load anthropometric data
anth <- read.csv("C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Data/abcd-data-release-5.1/core/physical-health/ph_y_anthro.csv", header = TRUE, sep = ",", dec = ".")
#View(anth)
library(psych)
#describe(anth$anthro_waist_cm) #Waist Circumference (inches)
#describe(anth$anthroheightcalc) #Standing Height Average (inches)
#Create key for Identifiers:
anth.varlabels <- anth[1,]
#Remove first row of Identifiers so we are left with only the data:
anth.data <- anth[-1,]
#Rename subject ID
anth.data$IID <- anth.data[,"src_subject_id"]
#Create 0, 1, 2, 3 assessment data sets
Baseline.anth<-anth.data[anth.data$eventname=="baseline_year_1_arm_1",]
Y1.anth<-anth.data[anth.data$eventname=="1_year_follow_up_y_arm_1",]
Y2.anth<-anth.data[anth.data$eventname=="2_year_follow_up_y_arm_1",]
Y3.anth<-anth.data[anth.data$eventname=="3_year_follow_up_y_arm_1",]
###############################################################################
################################ Baseline (10.5)###############################
###############################################################################
# First, merge Baseline.anth with Baseline.Identifiers based on IID
Merged.Baseline_anth_identifiers <- merge(Baseline.anth, Baseline.Identifiers, by = "IID")
# Then, merge Merged.Baseline_anth_identifiers with baseline.demo based on IID to get sex variable
Merged.Baseline_ID_anth <- merge(Merged.Baseline_anth_identifiers, Baseline.demo, by = "IID")
#Create Waist Circumference to Height Ratio Variable
Merged.Baseline_ID_anth$wchr <- Merged.Baseline_ID_anth$anthro_waist_cm/Merged.Baseline_ID_anth$anthroheightcalc
# Examine extreme scores
library(ggplot2)
summary(Merged.Baseline_ID_anth$wchr)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.2400 0.4314 0.4655 0.4811 0.5157 5.7500 14
Merged.Baseline_ID_anth <- Merged.Baseline_ID_anth[Merged.Baseline_ID_anth$wchr >= .20 & Merged.Baseline_ID_anth$wchr <= 1.5, ]
summary(Merged.Baseline_ID_anth$wchr)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.2400 0.4314 0.4655 0.4801 0.5156 1.3722 14
histogram_plot <- ggplot(Merged.Baseline_ID_anth, aes(x = wchr)) +
geom_histogram(binwidth = .05, fill = "blue", color = "black") +
labs(title = "Histogram of wchr", x = "wchr", y = "Frequency") +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10)
)
print(histogram_plot)
## Warning: Removed 14 rows containing non-finite values (`stat_bin()`).
# Regress age out of wchr score by sex separately
library(modelr)
library(tidyr)
library(dplyr)
Merged.Baseline_ID_anth$age <- Merged.Baseline_ID_anth$interview_age
# Subset the data
subset_data <- Merged.Baseline_ID_anth[, c("IID", "age", "sex", "wchr")]
# Make data set for boys and girls
boys_data <- subset_data[subset_data$sex == 1, ]
girls_data <- subset_data[subset_data$sex == 2, ]
######################## Regress wchr scores for boys ###########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(boys_data)
# Drop cases missing on wchr and age
boys_data <- boys_data[!is.na(boys_data$age) & !is.na(boys_data$wchr), ]
# Fit the linear regression model
model <- lm(wchr ~ age, data = boys_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into wchrt var
boys_data$wchrt <- residuals
# Display the modified data table with residuals
# print(boys_data)
######################## Regress wchr scores for girls ##########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(girls_data)
# Drop cases missing on wchr and age
girls_data <- girls_data[!is.na(girls_data$age) & !is.na(girls_data$wchr), ]
# Fit the linear regression model
model <- lm(wchr ~ age, data = girls_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into wchrt var
girls_data$wchrt <- residuals
# Display the modified data table with residuals
# print(girls_data)
# Merge Data
merged_data <- merge(boys_data, girls_data, by = "IID", all = TRUE, suffixes = c(".boys", ".girls"))
# Combine wchrt variables and create a new combined wchrt column
merged_data <- merged_data %>%
mutate(wchrt = coalesce(wchrt.boys, wchrt.girls))
# Display the merged data with combined wchrt variable
# print(merged_data)
# Subset to keep only IDD and wchrt score
merged_data <- merged_data[, c("IID", "wchrt")]
# Merge with raw data
merged_wchr <- merge(merged_data, Merged.Baseline_ID_anth, by = "IID", all = TRUE)
# print(merged_wchr)
# Rename variables to keep in each assessment data set
library(data.table)
variables_to_keep_Baseline <- c("IID","wchr","wchrt","age", "anthro_waist_cm", "anthroheightcalc")
# Convert the data frame to a data.table
setDT(merged_wchr)
# Subset the data table and create a new data table
subset_data <- merged_wchr[, ..variables_to_keep_Baseline]
# Rename the columns by appending "_Baseline"
colnames(subset_data) <- paste0(colnames(subset_data), "_Baseline")
# Rename IID for merge
colnames(subset_data)[colnames(subset_data) == "IID_Baseline"] <- "IID"
# rename data set for merge
wchr_Baseline <- subset_data
###############################################################################
################################ Year 1 (11.5) ################################
###############################################################################
# First, merge Y1.anth with Y1.Identifiers based on IID
Merged.Y1_anth_identifiers <- merge(Y1.anth, Y1.Identifiers, by = "IID")
# Then, merge Merged.Y1_anth_identifiers with Y1.demo based on IID to get sex variable
Merged.Y1_ID_anth <- merge(Merged.Y1_anth_identifiers, Baseline.demo, by = "IID")
#Create Waist Circumference to Height Ratio Variable
Merged.Y1_ID_anth$wchr <- Merged.Y1_ID_anth$anthro_waist_cm/Merged.Y1_ID_anth$anthroheightcalc
# Examine extreme scores
library(ggplot2)
summary(Merged.Y1_ID_anth$wchr)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.4304 0.4651 0.4821 0.5176 5.3333 42
Merged.Y1_ID_anth <- Merged.Y1_ID_anth[Merged.Y1_ID_anth$wchr >= .20 & Merged.Y1_ID_anth$wchr <= 1.5, ]
summary(Merged.Y1_ID_anth$wchr)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.2656 0.4304 0.4652 0.4816 0.5175 1.4746 42
histogram_plot <- ggplot(Merged.Y1_ID_anth, aes(x = wchr)) +
geom_histogram(binwidth = .05, fill = "blue", color = "black") +
labs(title = "Histogram of wchr", x = "wchr", y = "Frequency") +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10)
)
print(histogram_plot)
## Warning: Removed 42 rows containing non-finite values (`stat_bin()`).
# Regress age out of wchr score by sex separately
library(modelr)
library(tidyr)
library(dplyr)
Merged.Y1_ID_anth$age <- Merged.Y1_ID_anth$interview_age
# Subset the data
subset_data <- Merged.Y1_ID_anth[, c("IID", "age", "sex", "wchr")]
# Make data set for boys and girls
boys_data <- subset_data[subset_data$sex == 1, ]
girls_data <- subset_data[subset_data$sex == 2, ]
######################## Regress wchr scores for boys ###########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(boys_data)
# Drop cases missing on wchr and age
boys_data <- boys_data[!is.na(boys_data$age) & !is.na(boys_data$wchr), ]
# Fit the linear regression model
model <- lm(wchr ~ age, data = boys_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into wchrt var
boys_data$wchrt <- residuals
# Display the modified data table with residuals
# print(boys_data)
######################## Regress wchr scores for girls ##########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(girls_data)
# Drop cases missing on wchr and age
girls_data <- girls_data[!is.na(girls_data$age) & !is.na(girls_data$wchr), ]
# Fit the linear regression model
model <- lm(wchr ~ age, data = girls_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into wchrt var
girls_data$wchrt <- residuals
# Display the modified data table with residuals
# print(girls_data)
# Merge Data
merged_data <- merge(boys_data, girls_data, by = "IID", all = TRUE, suffixes = c(".boys", ".girls"))
# Combine wchrt variables and create a new combined wchrt column
merged_data <- merged_data %>%
mutate(wchrt = coalesce(wchrt.boys, wchrt.girls))
# Display the merged data with combined wchrt variable
# print(merged_data)
# Subset to keep only IDD and wchrt score
merged_data <- merged_data[, c("IID", "wchrt")]
# Merge with raw data
merged_wchr <- merge(merged_data, Merged.Y1_ID_anth , by = "IID", all = TRUE)
# print(merged_wchr)
# Rename variables to keep in each assessment data set
library(data.table)
variables_to_keep_Y1 <- c("IID","wchr","wchrt","age","anthro_waist_cm", "anthroheightcalc")
# Convert the data frame to a data.table
setDT(merged_wchr)
# Subset the data table and create a new data table
subset_data <- merged_wchr[, ..variables_to_keep_Y1]
# Rename the columns by appending "_Y1"
colnames(subset_data) <- paste0(colnames(subset_data), "_Y1")
# Rename IID for merge
colnames(subset_data)[colnames(subset_data) == "IID_Y1"] <- "IID"
# rename data set for merge
wchr_Y1 <- subset_data
###############################################################################
################################ Year 2 (12.5) ################################
###############################################################################
# First, merge Y2.anth with Y2.Identifiers based on IID
Merged.Y2_anth_identifiers <- merge(Y2.anth, Y2.Identifiers, by = "IID")
# Then, merge Merged.Y2_anth_identifiers with Y2.demo based on IID to get sex variable
Merged.Y2_ID_anth <- merge(Merged.Y2_anth_identifiers, Baseline.demo, by = "IID")
#Create Waist Circumference to Height Ratio Variable
Merged.Y2_ID_anth$wchr <- Merged.Y2_ID_anth$anthro_waist_cm/Merged.Y2_ID_anth$anthroheightcalc
# Examine extreme scores
library(ggplot2)
summary(Merged.Y2_ID_anth$wchr)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.4272 0.4643 0.4820 0.5226 4.6154 161
Merged.Y2_ID_anth <- Merged.Y2_ID_anth %>% filter(wchr >= 0.2)
Merged.Y2_ID_anth <- Merged.Y2_ID_anth %>% filter(wchr <= 1.5)
summary(Merged.Y2_ID_anth$wchr)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.2594 0.4273 0.4643 0.4816 0.5226 1.4062
histogram_plot <- ggplot(Merged.Y2_ID_anth, aes(x = wchr)) +
geom_histogram(binwidth = .05, fill = "blue", color = "black") +
labs(title = "Histogram of wchr", x = "wchr", y = "Frequency") +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10)
)
print(histogram_plot)
# Regress age out of wchr score by sex separately
library(modelr)
library(tidyr)
library(dplyr)
Merged.Y2_ID_anth$age <- Merged.Y2_ID_anth$interview_age
# Subset the data
subset_data <- Merged.Y2_ID_anth[, c("IID", "age", "sex", "wchr")]
# Make data set for boys and girls
boys_data <- subset_data[subset_data$sex == 1, ]
girls_data <- subset_data[subset_data$sex == 2, ]
######################## Regress wchr scores for boys ###########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(boys_data)
# Drop cases missing on wchr and age
boys_data <- boys_data[!is.na(boys_data$age) & !is.na(boys_data$wchr), ]
# Fit the linear regression model
model <- lm(wchr ~ age, data = boys_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into wchrt var
boys_data$wchrt <- residuals
# Display the modified data table with residuals
# print(boys_data)
######################## Regress wchr scores for girls ##########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(girls_data)
# Drop cases missing on wchr and age
girls_data <- girls_data[!is.na(girls_data$age) & !is.na(girls_data$wchr), ]
# Fit the linear regression model
model <- lm(wchr ~ age, data = girls_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into wchrt var
girls_data$wchrt <- residuals
# Display the modified data table with residuals
# print(girls_data)
# Merge Data
merged_data <- merge(boys_data, girls_data, by = "IID", all = TRUE, suffixes = c(".boys", ".girls"))
# Combine wchrt variables and create a new combined wchrt column
merged_data <- merged_data %>%
mutate(wchrt = coalesce(wchrt.boys, wchrt.girls))
# Display the merged data with combined wchrt variable
# print(merged_data)
# Subset to keep only IDD and wchrt score
merged_data <- merged_data[, c("IID","wchrt")]
# Merge with CDC calculated data set
merged_wchr <- merge(merged_data, Merged.Y2_ID_anth , by = "IID", all = TRUE)
# print(merged_wchr)
# Rename variables to keep in each assessment data set
library(data.table)
variables_to_keep_Y2 <- c("IID","wchr","wchrt","age","anthro_waist_cm", "anthroheightcalc")
# Convert the data frame to a data.table
setDT(merged_wchr)
# Subset the data table and create a new data table
subset_data <- merged_wchr[, ..variables_to_keep_Y2]
# Rename the columns by appending "_Y2"
colnames(subset_data) <- paste0(colnames(subset_data), "_Y2")
# Rename IID for merge
colnames(subset_data)[colnames(subset_data) == "IID_Y2"] <- "IID"
# rename data set for merge
wchr_Y2 <- subset_data
###############################################################################
################################ Year 3 (13.5) ################################
###############################################################################
# First, merge Y3.anth with Y3.Identifiers based on IID
Merged.Y3_anth_identifiers <- merge(Y3.anth, Y3.Identifiers, by = "IID")
# Then, merge Merged.Y3_anth_identifiers with Y3.demo based on IID to get sex variable
Merged.Y3_ID_anth <- merge(Merged.Y3_anth_identifiers, Baseline.demo, by = "IID")
#Create Waist Circumference to Height Ratio Variable
Merged.Y3_ID_anth$wchr <- Merged.Y3_ID_anth$anthro_waist_cm/Merged.Y3_ID_anth$anthroheightcalc
# Examine extreme scores
library(ggplot2)
summary(Merged.Y3_ID_anth$wchr)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.4291 0.4685 0.4925 0.5342 5.5000 1795
Merged.Y3_ID_anth <- Merged.Y3_ID_anth %>% filter(wchr >= 0.2)
Merged.Y3_ID_anth <- Merged.Y3_ID_anth %>% filter(wchr <= 1.5)
summary(Merged.Y3_ID_anth$wchr)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.3437 0.4295 0.4686 0.4904 0.5341 1.2629
histogram_plot <- ggplot(Merged.Y3_ID_anth, aes(x = wchr)) +
geom_histogram(binwidth = .05, fill = "blue", color = "black") +
labs(title = "Histogram of wchr", x = "wchr", y = "Frequency") +
theme_minimal() +
theme(
plot.title = element_text(face = "bold", size = 14),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10)
)
print(histogram_plot)
# Regress age out of wchr score by sex separately
library(modelr)
library(tidyr)
library(dplyr)
Merged.Y3_ID_anth$age <- Merged.Y3_ID_anth$interview_age
# Subset the data
subset_data <- Merged.Y3_ID_anth[, c("IID", "age", "sex", "wchr")]
# Make data set for boys and girls
boys_data <- subset_data[subset_data$sex == 1, ]
girls_data <- subset_data[subset_data$sex == 2, ]
######################## Regress wchr scores for boys ###########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(boys_data)
# Drop cases missing on wchr and age
boys_data <- boys_data[!is.na(boys_data$age) & !is.na(boys_data$wchr), ]
# Fit the linear regression model
model <- lm(wchr ~ age, data = boys_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into wchrt var
boys_data$wchrt <- residuals
# Display the modified data table with residuals
# print(boys_data)
######################## Regress wchr scores for girls ##########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(girls_data)
# Drop cases missing on wchr and age
girls_data <- girls_data[!is.na(girls_data$age) & !is.na(girls_data$wchr), ]
# Fit the linear regression model
model <- lm(wchr ~ age, data = girls_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals into wchrt var
girls_data$wchrt <- residuals
# Display the modified data table with residuals
# print(girls_data)
# Merge Data
merged_data <- merge(boys_data, girls_data, by = "IID", all = TRUE, suffixes = c(".boys", ".girls"))
# Combine wchrt variables and create a new combined wchrt column
merged_data <- merged_data %>%
mutate(wchrt = coalesce(wchrt.boys, wchrt.girls))
# Display the merged data with combined wchrt variable
# print(merged_data)
# Subset to keep only IDD and wchrt score
merged_data <- merged_data[, c("IID", "wchrt")]
# Merge with raw data
merged_wchr <- merge(merged_data, Merged.Y3_ID_anth , by = "IID", all = TRUE)
# print(merged_wchr)
# Rename variables to keep in each assessment data set
library(data.table)
variables_to_keep_Y3 <- c("IID","wchr","wchrt","age","anthro_waist_cm", "anthroheightcalc")
# Convert the data frame to a data.table
setDT(merged_wchr)
# Subset the data table and create a new data table
subset_data <- merged_wchr[, ..variables_to_keep_Y3]
# Rename the columns by appending "_Y3"
colnames(subset_data) <- paste0(colnames(subset_data), "_Y3")
# Rename IID for merge
colnames(subset_data)[colnames(subset_data) == "IID_Y3"] <- "IID"
# rename data set for merge
wchr_Y3 <- subset_data
##### Merge all Waist Height to Weight Ratio Together data sets together ######
# Merge all wchr data sets together
library(data.table)
# List of datasets to merge
wchr_datasets <- list(wchr_Baseline, wchr_Y1, wchr_Y2, wchr_Y3)
# Merge all data.tables using full outer join based on IID
wchr_merged_data <- Reduce(function(x, y) merge(x, y, by = "IID", all = TRUE), wchr_datasets)
# wchr Descriptive Statistics
library(psych)
# Select the variables you want to describe
variables_to_describe <- c("age_Baseline", "wchr_Baseline", "wchrt_Baseline",
"age_Y1", "wchr_Y1", "wchrt_Y1",
"age_Y2", "wchr_Y2", "wchrt_Y2",
"age_Y3", "wchr_Y3", "wchrt_Y3")
# Convert data.table to data frame
data_frame <- as.data.frame(wchr_merged_data)
# Use the describe() function to obtain descriptive statistics
variable_descriptions <- describe(data_frame[variables_to_describe])
# Print the descriptive statistics
print(variable_descriptions)
## vars n mean sd median trimmed mad min max range
## age_Baseline 1 10345 118.98 7.49 119.00 118.87 10.38 107.00 133.00 26.00
## wchr_Baseline 2 10345 0.48 0.07 0.47 0.47 0.06 0.24 1.37 1.13
## wchrt_Baseline 3 10342 0.00 0.07 -0.01 -0.01 0.06 -0.24 0.89 1.13
## age_Y1 4 9714 131.07 7.72 131.00 130.99 10.38 117.00 149.00 32.00
## wchr_Y1 5 9714 0.48 0.08 0.47 0.47 0.06 0.27 1.47 1.21
## wchrt_Y1 6 9711 0.00 0.08 -0.02 -0.01 0.06 -0.21 1.00 1.21
## age_Y2 7 7919 143.77 7.85 144.00 143.70 10.38 127.00 166.00 39.00
## wchr_Y2 8 7919 0.48 0.08 0.46 0.47 0.07 0.26 1.41 1.15
## wchrt_Y2 9 7917 0.00 0.08 -0.02 -0.01 0.07 -0.22 0.92 1.14
## age_Y3 10 1755 155.33 7.45 155.00 155.34 8.90 139.00 174.00 35.00
## wchr_Y3 11 1755 0.49 0.09 0.47 0.48 0.07 0.34 1.26 0.92
## wchrt_Y3 12 1754 0.00 0.08 -0.02 -0.01 0.07 -0.15 0.77 0.92
## skew kurtosis se
## age_Baseline 0.07 -1.26 0.07
## wchr_Baseline 1.31 4.92 0.00
## wchrt_Baseline 1.31 4.92 0.00
## age_Y1 0.07 -1.19 0.08
## wchr_Y1 2.05 13.96 0.00
## wchrt_Y1 2.05 13.98 0.00
## age_Y2 0.08 -1.01 0.09
## wchr_Y2 1.52 6.84 0.00
## wchrt_Y2 1.52 6.77 0.00
## age_Y3 0.01 -1.08 0.18
## wchr_Y3 1.72 7.24 0.00
## wchrt_Y3 1.72 7.30 0.00
# Create four-panel histogram
library(ggplot2)
# Combine all wchr variables into a single column
all_wchr <- c(data_frame$wchr_Baseline, data_frame$wchr_Y1, data_frame$wchr_Y2, data_frame$wchr_Y3)
# Create a data frame with the combined wchr values
combined_data <- data.frame(wchr = all_wchr, Timepoint = rep(c("Baseline", "Y1", "Y2", "Y3"), each = nrow(data_frame)))
# Create the combined histogram with APA-style formatting
ggplot(combined_data, aes(x = wchr)) +
geom_histogram(binwidth = .5, fill = "#0072B2", color = "#0072B2", alpha = 0.7) +
labs(title = "Histogram of Combined wchr", x = "wchr", y = "Frequency") +
facet_wrap(~ Timepoint, scales = "free_x", nrow = 2) +
theme_minimal() +
theme(panel.grid = element_blank(),
panel.border = element_rect(color = "black", fill = NA),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10),
strip.text = element_text(size = 12),
plot.title = element_text(size = 14, hjust = 0.5),
strip.background = element_rect(fill = "#E5E5E5"),
legend.position = "none")
## Warning: Removed 14075 rows containing non-finite values (`stat_bin()`).
################################################################################
# install.packages("corrplot")
library(corrplot)
library(dplyr)
library(ggplot2)
library(reshape2)
# Select the wchr variables
wchr_variables <- c("wchr_Baseline", "wchr_Y1", "wchr_Y2", "wchr_Y3")
# Subset the data for the selected wchr variables
wchr_data <- data_frame %>%
select(all_of(wchr_variables))
# Compute the correlation matrix
cor_matrix <- cor(wchr_data, use = "complete.obs")
cor_matrix
## wchr_Baseline wchr_Y1 wchr_Y2 wchr_Y3
## wchr_Baseline 1.0000000 0.7383546 0.7041304 0.6793967
## wchr_Y1 0.7383546 1.0000000 0.7886294 0.7572817
## wchr_Y2 0.7041304 0.7886294 1.0000000 0.7901607
## wchr_Y3 0.6793967 0.7572817 0.7901607 1.0000000
# Create a ggheatmap
ggheatmap <- ggplot(melt(cor_matrix), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 4) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
################################################################################
# Convert to long to examine trajectories
library(tidyr)
# Subset
long_data <- data_frame[, c("IID", "wchr_Baseline", "wchr_Y1", "wchr_Y2", "wchr_Y3")]
library(reshape2)
# Convert wide data to long format using melt()
long_data <- melt(data_frame, id.vars = "IID", measure.vars = c("wchr_Baseline", "wchr_Y1", "wchr_Y2", "wchr_Y3"),
variable.name = "Timepoint", value.name = "wchr")
# Sort the long-format data by IID
sorted_long_data <- long_data %>%
arrange(IID)
# Print the long-format data
# print(long_data)
library(dplyr)
library(ggplot2)
# Randomly select 1000 unique IIDs
unique_iids <- sorted_long_data %>%
distinct(IID) %>%
sample_n(200) # Select 1000 random IIDs
# Subset the data based on the selected IIDs
subset_data <- sorted_long_data %>%
filter(IID %in% unique_iids$IID)
# Create a longitudinal plot of wchr across timepoints
ggplot(subset_data, aes(x = Timepoint, y = wchr, group = IID, color = IID)) +
geom_line(size = 1) +
labs(title = "Longitudinal Plot of wchr Across Timepoint (Subset)", x = "Timepoint", y = "wchr") +
theme_minimal() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(color = "black", fill = NA),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10),
plot.title = element_text(size = 14, hjust = 0.5))
## Warning: Removed 202 rows containing missing values (`geom_line()`).
#Load PDS data
pub <- read.csv("C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Data/abcd-data-release-5.1/core/physical-health/ph_y_pds.csv", header = TRUE, sep = ",", dec = ".")
#View(pds)
describe(pub$pds_y_ss_female_category)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6445 2.41 0.91 3 2.41 1.48 1 5 4 -0.22 -0.79 0.01
describe(pub$pds_y_ss_male_category)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 8242 1.95 0.77 2 1.92 1.48 1 5 4 0.4 -0.29 0.01
describe(pub$pds_y_ss_female_category_2)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 20170 3.09 1 3 3.19 1.48 1 5 4 -0.56 -0.25 0.01
describe(pub$pds_y_ss_male_cat_2)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 24190 2.3 0.89 2 2.27 1.48 1 5 4 0.15 -0.68 0.01
#Create key for Identifiers:
pub.varlabels <- pub[1,]
#Remove first row of Identifiers so we are left with only the data:
pub.data <- pub[-1,]
#Rename subject ID
pub.data$IID <- pub.data[,"src_subject_id"]
#Create 0, 1, 2,3 assessment data sets
Baseline.pub<-pub.data[pub.data$eventname=="baseline_year_1_arm_1",]
Y1.pub<-pub.data[pub.data$eventname=="1_year_follow_up_y_arm_1",]
Y2.pub<-pub.data[pub.data$eventname=="2_year_follow_up_y_arm_1",]
Y3.pub<-pub.data[pub.data$eventname=="3_year_follow_up_y_arm_1",]
###############################################################################
################################ Baseline (10.5)###############################
###############################################################################
# Merge Baseline.pub with Baseline.Identifiers based on IID to get age
Merged.Baseline_pub_identifiers <- merge(Baseline.pub, Baseline.Identifiers, by = "IID")
# Then, merge Merged.Baseline_pub_identifiers with baseline.demo based on IID to get sex variable
Merged.Baseline_ID_pub <- merge(Merged.Baseline_pub_identifiers, Baseline.demo, by = "IID")
############### Regress age out of BMI score by sex separately #################
library(modelr)
library(tidyr)
library(dplyr)
# Rename variables for code below
colnames(Merged.Baseline_ID_pub)[colnames(Merged.Baseline_ID_pub) == "interview_age"] <- "age"
colnames(Merged.Baseline_ID_pub)[colnames(Merged.Baseline_ID_pub) == "pds_y_ss_female_category"] <- "pub_girls"
colnames(Merged.Baseline_ID_pub)[colnames(Merged.Baseline_ID_pub) == "pds_y_ss_male_category"] <- "pub_boys"
# Subset the data
pub_subset <- Merged.Baseline_ID_pub[, c("IID", "age", "sex", "pub_girls", "pub_boys" )]
# Make data set for boys and girls
boys_data <- pub_subset[pub_subset$sex == 1, ]
girls_data <- pub_subset[pub_subset$sex == 2, ]
#################### Regress PDS scores for boys #####################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(boys_data)
# Drop cases missing on pub and age
boys_data <- boys_data[!is.na(boys_data$age) & !is.na(boys_data$pub_boys), ]
# Fit the linear regression model
model <- lm(pub_boys ~ age, data = boys_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals pubo pubt var
boys_data$pubt_boys <- residuals
# Display the modified data table with residuals
#print(boys_data)
######################## Regress pub scores for girls ##########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(girls_data)
# Drop cases missing on pub and age
girls_data <- girls_data[!is.na(girls_data$age) & !is.na(girls_data$pub_girls), ]
# Fit the linear regression model
model <- lm(pub_girls ~ age, data = girls_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals pubo pubt var
girls_data$pubt_girls <- residuals
# Display the modified data table with residuals
# print(girls_data)
################################################################################
# Merge Data
merged_data <- merge(boys_data, girls_data, by = "IID", all = TRUE, suffixes = c(".boys", ".girls"))
# Combine pubt variables and create a new combined pubt column
merged_data <- merged_data %>%
mutate(pubt = coalesce(pubt_boys, pubt_girls))
# Display the merged data with combined pubt variable
# print(merged_data)
# Subset to only IDD and pubt score
merged_data <- merged_data[, c("IID", "pubt")]
# Merge with raw pub data
merged_pub <- merge(pub_subset, merged_data, by = "IID", all = TRUE)
# print(merged_pub)
# Combine pub boys and girls variables and create a new combined pub column
merged_pub <- merged_pub %>%
mutate(pub = coalesce(pub_boys, pub_girls))
# Rename variables to keep in each assessment data set
library(data.table)
variables_to_keep_Baseline <- c("IID","pub", "pubt", "age")
# Convert the data frame to a data.table
setDT(merged_pub)
# Subset the data table and create a new data table
subset_data <- merged_pub[, ..variables_to_keep_Baseline]
# Rename the columns by appending "_Baseline"
colnames(subset_data) <- paste0(colnames(subset_data), "_Baseline")
# Rename IID for merge
colnames(subset_data)[colnames(subset_data) == "IID_Baseline"] <- "IID"
# rename data set for merge
pub_Baseline <- subset_data
###############################################################################
################################ Year 1 (11.5) ################################
###############################################################################
# Merge Y1.pub with Baseline.Identifiers based on IID to get age
Merged.Y1_pub_identifiers <- merge(Y1.pub, Baseline.Identifiers, by = "IID")
# Then, merge Merged.Baseline_pub_identifiers with baseline.demo based on IID to get sex variable
Merged.Y1_ID_pub <- merge(Merged.Y1_pub_identifiers, Baseline.demo, by = "IID")
############### Regress age out of PDS score by sex separately #################
library(modelr)
library(tidyr)
library(dplyr)
# Rename variables for code below
colnames(Merged.Y1_ID_pub)[colnames(Merged.Y1_ID_pub) == "interview_age"] <- "age"
colnames(Merged.Y1_ID_pub)[colnames(Merged.Y1_ID_pub) == "pds_y_ss_female_category"] <- "pub_girls"
colnames(Merged.Y1_ID_pub)[colnames(Merged.Y1_ID_pub) == "pds_y_ss_male_category"] <- "pub_boys"
# Subset the data
pub_subset <- Merged.Y1_ID_pub[, c("IID", "age", "sex", "pub_girls", "pub_boys" )]
# Make data set for boys and girls
boys_data <- pub_subset[pub_subset$sex == 1, ]
girls_data <- pub_subset[pub_subset$sex == 2, ]
#################### Regress PDS scores for boys #####################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(boys_data)
# Drop cases missing on pub and age
boys_data <- boys_data[!is.na(boys_data$age) & !is.na(boys_data$pub_boys), ]
# Fit the linear regression model
model <- lm(pub_boys ~ age, data = boys_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals pubo pubt var
boys_data$pubt_boys <- residuals
# Display the modified data table with residuals
# print(boys_data)
######################## Regress pub scores for girls ##########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(girls_data)
# Drop cases missing on pub and age
girls_data <- girls_data[!is.na(girls_data$age) & !is.na(girls_data$pub_girls), ]
# Fit the linear regression model
model <- lm(pub_girls ~ age, data = girls_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals pubo pubt var
girls_data$pubt_girls <- residuals
# Display the modified data table with residuals
# print(girls_data)
################################################################################
# Merge Data
merged_data <- merge(boys_data, girls_data, by = "IID", all = TRUE, suffixes = c(".boys", ".girls"))
# Combine pubt variables and create a new combined pubt column
merged_data <- merged_data %>%
mutate(pubt = coalesce(pubt_boys, pubt_girls))
# Display the merged data with combined pubt variable
# print(merged_data)
# Subset to only IDD and pubt score
merged_data <- merged_data[, c("IID", "pubt")]
# Merge with raw pub data
merged_pub <- merge(pub_subset, merged_data, by = "IID", all = TRUE)
# print(merged_pub)
# Combine pub boys and girls variables and create a new combined pub column
merged_pub <- merged_pub %>%
mutate(pub = coalesce(pub_boys, pub_girls))
# Rename variables to keep in each assessment data set
library(data.table)
variables_to_keep_Y1 <- c("IID","pub", "pubt", "age")
# Convert the data frame to a data.table
setDT(merged_pub)
# Subset the data table and create a new data table
subset_data <- merged_pub[, ..variables_to_keep_Y1]
# Rename the columns by appending "_Y1"
colnames(subset_data) <- paste0(colnames(subset_data), "_Y1")
# Rename IID for merge
colnames(subset_data)[colnames(subset_data) == "IID_Y1"] <- "IID"
# rename data set for merge
pub_Y1 <- subset_data
###############################################################################
################################ Year 2 (12.5) ################################
###############################################################################
# Merge Y2.pub with Baseline.Identifiers based on IID to get age
Merged.Y2_pub_identifiers <- merge(Y2.pub, Y2.Identifiers, by = "IID")
# Then, merge Merged.Baseline_pub_identifiers with baseline.demo based on IID to get sex variable
Merged.Y2_ID_pub <- merge(Merged.Y2_pub_identifiers, Baseline.demo, by = "IID")
############### Regress age out of PDS score by sex separately #################
library(modelr)
library(tidyr)
library(dplyr)
#NOTE USING _2 PDS score since massive missing on one without _2 NEED TO KNOW DIFFERENCES
# Rename variables for code below
colnames(Merged.Y2_ID_pub)[colnames(Merged.Y2_ID_pub) == "interview_age"] <- "age"
colnames(Merged.Y2_ID_pub)[colnames(Merged.Y2_ID_pub) == "pds_y_ss_female_category_2"] <- "pub_girls"
colnames(Merged.Y2_ID_pub)[colnames(Merged.Y2_ID_pub) == "pds_y_ss_male_cat_2"] <- "pub_boys"
#summary(Merged.Y2_ID_pub$pub_boys)
# Subset the data
pub_subset <- Merged.Y2_ID_pub[, c("IID", "age", "sex", "pub_girls", "pub_boys")]
# Make data set for boys and girls
boys_data <- pub_subset[pub_subset$sex == 1, ]
girls_data <- pub_subset[pub_subset$sex == 2, ]
#################### Regress PDS scores for boys #####################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(boys_data)
# Drop cases missing on pub and age
boys_data <- boys_data[!is.na(boys_data$age) & !is.na(boys_data$pub_boys), ]
# Fit the linear regression model
model <- lm(pub_boys ~ age, data = boys_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals pubo pubt var
boys_data$pubt_boys <- residuals
# Display the modified data table with residuals
# print(boys_data)
######################## Regress pub scores for girls ##########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(girls_data)
# Drop cases missing on pub and age
girls_data <- girls_data[!is.na(girls_data$age) & !is.na(girls_data$pub_girls), ]
# Fit the linear regression model
model <- lm(pub_girls ~ age, data = girls_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals pubo pubt var
girls_data$pubt_girls <- residuals
# Display the modified data table with residuals
# print(girls_data)
################################################################################
# Merge Data
merged_data <- merge(boys_data, girls_data, by = "IID", all = TRUE, suffixes = c(".boys", ".girls"))
# Combine pubt variables and create a new combined pubt column
merged_data <- merged_data %>%
mutate(pubt = coalesce(pubt_boys, pubt_girls))
# Display the merged data with combined pubt variable
# print(merged_data)
# Subset to only IDD and pubt score
merged_data <- merged_data[, c("IID", "pubt")]
# Merge with raw pub data
merged_pub <- merge(pub_subset, merged_data, by = "IID", all = TRUE)
# print(merged_pub)
# Combine pub boys and girls variables and create a new combined pub column
merged_pub <- merged_pub %>%
mutate(pub = coalesce(pub_boys, pub_girls))
# Rename variables to keep in each assessment data set
library(data.table)
variables_to_keep_Y2 <- c("IID","pub", "pubt", "age")
# Convert the data frame to a data.table
setDT(merged_pub)
# Subset the data table and create a new data table
subset_data <- merged_pub[, ..variables_to_keep_Y2]
# Rename the columns by appending "_Y2"
colnames(subset_data) <- paste0(colnames(subset_data), "_Y2")
# Rename IID for merge
colnames(subset_data)[colnames(subset_data) == "IID_Y2"] <- "IID"
# rename data set for merge
pub_Y2 <- subset_data
###############################################################################
################################ Year 3 (13.5) ################################
###############################################################################
# Merge Y3.pub with Baseline.Identifiers based on IID to get age
Merged.Y3_pub_identifiers <- merge(Y3.pub, Baseline.Identifiers, by = "IID")
# Then, merge Merged.Baseline_pub_identifiers with baseline.demo based on IID to get sex variable
Merged.Y3_ID_pub <- merge(Merged.Y3_pub_identifiers, Baseline.demo, by = "IID")
############### Regress age out of BMI score by sex separately #################
library(modelr)
library(tidyr)
library(dplyr)
# summary(Merged.Y3_ID_pub$pds_y_ss_female_category)
# summary(Merged.Y3_ID_pub$pds_y_ss_female_category_2) #Use this one
# summary(Merged.Y3_ID_pub$pds_y_ss_male_category)
# summary(Merged.Y3_ID_pub$pds_y_ss_male_cat_2) #Use this one
# Rename variables for code below
colnames(Merged.Y3_ID_pub)[colnames(Merged.Y3_ID_pub) == "interview_age"] <- "age"
colnames(Merged.Y3_ID_pub)[colnames(Merged.Y3_ID_pub) == "pds_y_ss_female_category_2"] <- "pub_girls"
colnames(Merged.Y3_ID_pub)[colnames(Merged.Y3_ID_pub) == "pds_y_ss_male_cat_2"] <- "pub_boys"
# Subset the data
pub_subset <- Merged.Y3_ID_pub[, c("IID", "age", "sex", "pub_girls", "pub_boys" )]
# Make data set for boys and girls
boys_data <- pub_subset[pub_subset$sex == 1, ]
girls_data <- pub_subset[pub_subset$sex == 2, ]
#################### Regress PDS scores for boys #####################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(boys_data)
# Drop cases missing on pub and age
boys_data <- boys_data[!is.na(boys_data$age) & !is.na(boys_data$pub_boys), ]
# Fit the linear regression model
model <- lm(pub_boys ~ age, data = boys_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals pubo pubt var
boys_data$pubt_boys <- residuals
# Display the modified data table with residuals
# print(boys_data)
######################## Regress pub scores for girls ##########################
# Convert data.table to data.frame for linear regression
data_frame <- as.data.frame(girls_data)
# Drop cases missing on pub and age
girls_data <- girls_data[!is.na(girls_data$age) & !is.na(girls_data$pub_girls), ]
# Fit the linear regression model
model <- lm(pub_girls ~ age, data = girls_data)
# Calculate residuals
residuals <- residuals(model)
# Save residuals pubo pubt var
girls_data$pubt_girls <- residuals
# Display the modified data table with residuals
# print(girls_data)
################################################################################
# Merge Data
merged_data <- merge(boys_data, girls_data, by = "IID", all = TRUE, suffixes = c(".boys", ".girls"))
# Combine pubt variables and create a new combined pubt column
merged_data <- merged_data %>%
mutate(pubt = coalesce(pubt_boys, pubt_girls))
# Display the merged data with combined pubt variable
# print(merged_data)
# Subset to only IDD and pubt score
merged_data <- merged_data[, c("IID", "pubt")]
# Merge with raw pub data
merged_pub <- merge(pub_subset, merged_data, by = "IID", all = TRUE)
# print(merged_pub)
# Combine pub boys and girls variables and create a new combined pub column
merged_pub <- merged_pub %>%
mutate(pub = coalesce(pub_boys, pub_girls))
# Rename variables to keep in each assessment data set
library(data.table)
variables_to_keep_Y3 <- c("IID","pub", "pubt", "age")
# Convert the data frame to a data.table
setDT(merged_pub)
# Subset the data table and create a new data table
subset_data <- merged_pub[, ..variables_to_keep_Y3]
# Rename the columns by appending "_Y3"
colnames(subset_data) <- paste0(colnames(subset_data), "_Y3")
# Rename IID for merge
colnames(subset_data)[colnames(subset_data) == "IID_Y3"] <- "IID"
# rename data set for merge
pub_Y3 <- subset_data
############### Merge all PDS data sets together ####################
# Merge all pub data sets together
library(data.table)
# List of datasets to merge
pub_datasets <- list(pub_Baseline, pub_Y1, pub_Y2, pub_Y3)
# Merge all data.tables using full outer join based on IID
pub_merged_data <- Reduce(function(x, y) merge(x, y, by = "IID", all = TRUE), pub_datasets)
# Puberty Descriptive Statistics
library(psych)
# Select the variables you want to describe
variables_to_describe <- c("age_Baseline", "pub_Baseline", "pubt_Baseline",
"age_Y1", "pub_Y1", "pubt_Y1",
"age_Y2", "pub_Y2", "pubt_Y2",
"age_Y3", "pub_Y3", "pubt_Y3")
# Convert data.table to data frame
data_frame <- as.data.frame(pub_merged_data)
# Use the describe() function to obtain descriptive statistics
variable_descriptions <- describe(data_frame[variables_to_describe])
# Print the descriptive statistics
print(variable_descriptions)
## vars n mean sd median trimmed mad min max range
## age_Baseline 1 10366 118.97 7.49 119.00 118.87 10.38 107.00 133.00 26.00
## pub_Baseline 2 8397 2.08 0.83 2.00 2.06 1.48 1.00 5.00 4.00
## pubt_Baseline 3 8363 0.00 0.81 0.05 -0.01 1.05 -1.61 3.08 4.69
## age_Y1 4 9802 118.97 7.51 119.00 118.87 10.38 107.00 133.00 26.00
## pub_Y1 5 4247 2.27 0.90 2.00 2.25 1.48 1.00 5.00 4.00
## pubt_Y1 6 4224 0.00 0.80 0.04 0.00 0.95 -2.12 3.08 5.21
## age_Y2 7 9585 144.30 8.02 144.00 144.20 10.38 127.00 168.00 41.00
## pub_Y2 8 9067 2.69 0.97 3.00 2.73 1.48 1.00 5.00 4.00
## pubt_Y2 9 9064 0.00 0.77 0.04 0.02 0.75 -2.84 3.02 5.86
## age_Y3 10 9027 118.97 7.50 119.00 118.87 10.38 107.00 133.00 26.00
## pub_Y3 11 8595 3.12 0.93 3.00 3.20 1.48 1.00 5.00 4.00
## pubt_Y3 12 8592 0.00 0.74 0.11 0.03 0.69 -2.94 2.53 5.47
## skew kurtosis se
## age_Baseline 0.07 -1.26 0.07
## pub_Baseline 0.19 -0.75 0.01
## pubt_Baseline 0.13 -0.58 0.01
## age_Y1 0.07 -1.27 0.08
## pub_Y1 0.09 -0.76 0.01
## pubt_Y1 -0.04 -0.38 0.01
## age_Y2 0.11 -0.93 0.08
## pub_Y2 -0.17 -0.73 0.01
## pubt_Y2 -0.28 0.11 0.01
## age_Y3 0.07 -1.27 0.08
## pub_Y3 -0.52 -0.27 0.01
## pubt_Y3 -0.46 0.55 0.01
# Create four-panel histogram
library(ggplot2)
# Combine all pub variables pubo a single column
all_pub <- c(data_frame$pub_Baseline, data_frame$pub_Y1, data_frame$pub_Y2, data_frame$pub_Y3)
# Create a data frame with the combined pub values
combined_data <- data.frame(pub = all_pub, Timepopub = rep(c("Baseline", "Y1", "Y2", "Y3"), each = nrow(data_frame)))
# Create the combined histogram with APA-style formatting
ggplot(combined_data, aes(x = pub)) +
geom_histogram(binwidth = 1, fill = "#0072B2", color = "#0072B2", alpha = 0.7) +
labs(title = "Histogram of Combined pub", x = "pub", y = "Frequency") +
facet_wrap(~ Timepopub, scales = "free_x", nrow = 2) +
theme_minimal() +
theme(panel.grid = element_blank(),
panel.border = element_rect(color = "black", fill = NA),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10),
strip.text = element_text(size = 12),
plot.title = element_text(size = 14, hjust = 0.5),
strip.background = element_rect(fill = "#E5E5E5"),
legend.position = "none")
## Warning: Removed 11158 rows containing non-finite values (`stat_bin()`).
################################################################################
# install.packages("corrplot")
library(corrplot)
library(dplyr)
library(ggplot2)
library(reshape2)
# Select the pub variables
pub_variables <- c("pub_Baseline", "pub_Y1", "pub_Y2", "pub_Y3")
# Subset the data for the selected pub variables
pub_data <- data_frame %>%
select(all_of(pub_variables))
# Compute the correlation matrix
cor_matrix <- cor(pub_data, use = "complete.obs")
cor_matrix
## pub_Baseline pub_Y1 pub_Y2 pub_Y3
## pub_Baseline 1.0000000 0.4450122 0.3553012 0.2729983
## pub_Y1 0.4450122 1.0000000 0.6236506 0.5228071
## pub_Y2 0.3553012 0.6236506 1.0000000 0.6822757
## pub_Y3 0.2729983 0.5228071 0.6822757 1.0000000
# Create a ggheatmap
ggheatmap <- ggplot(melt(cor_matrix), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 4) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
################################################################################
# Convert to long to examine trajectories
library(tidyr)
# Subset
long_data <- data_frame[, c("IID", "pub_Baseline", "pub_Y1", "pub_Y2", "pub_Y3")]
library(reshape2)
# Convert wide data to long format using melt()
long_data <- melt(data_frame, id.vars = "IID", measure.vars = c("pub_Baseline", "pub_Y1", "pub_Y2", "pub_Y3"),
variable.name = "Timepopub", value.name = "pub")
# Sort the long-format data by IID
sorted_long_data <- long_data %>%
arrange(IID)
# Print the long-format data
# print(long_data)
library(dplyr)
library(ggplot2)
# Randomly select 1000 unique IIDs
unique_iids <- sorted_long_data %>%
distinct(IID) %>%
sample_n(500) # Select 1000 random IIDs
# Subset the data based on the selected IIDs
subset_data <- sorted_long_data %>%
filter(IID %in% unique_iids$IID)
# Create a longitudinal plot of pub across timepopubs
ggplot(subset_data, aes(x = Timepopub, y = pub, group = IID, color = IID)) +
geom_line(size = 1) +
labs(title = "Longitudinal Plot of pub Across Time (Subset)", x = "Timepoint", y = "pub") +
theme_minimal() +
theme(legend.position = "none",
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_rect(color = "black", fill = NA),
axis.title = element_text(size = 12),
axis.text = element_text(size = 10),
plot.title = element_text(size = 14, hjust = 0.5))
## Warning: Removed 307 rows containing missing values (`geom_line()`).
library(psych)
# Load parent psychopathology covariate data
abcl <- read.csv("C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Data/abcd-data-release-5.1/core/mental-health/mh_p_abcl.csv", header = TRUE, sep = ",", dec = ".")
asr <- read.csv("C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Data/abcd-data-release-5.1/core/mental-health/mh_p_asr.csv", header = TRUE, sep = ",", dec = ".")
# View(abcl)
# View(asr)
# Remove first row of Identifiers so we are left with only the data:
abcl.data <- abcl[-1,]
asr.data <- asr[-1,]
# Rename subject ID
abcl.data$IID <- abcl.data[,"src_subject_id"]
asr.data$IID <- asr.data[,"src_subject_id"]
# Create Y2 assessment data sets for parent psychopathology (Other Parent)
Y2.abcl<-abcl.data[abcl.data$eventname=="2_year_follow_up_y_arm_1",]
# Create 0, 2 assessment data sets for parent psychopathology (Self report)
Baseline.asr<-asr.data[asr.data$eventname=="baseline_year_1_arm_1",]
Y2.asr<-asr.data[asr.data$eventname=="2_year_follow_up_y_arm_1",]
# Rename variables for merge
Baseline.asr$asr_scr_internal_r_b <- Baseline.asr$asr_scr_internal_r
Y2.asr$asr_scr_internal_r_2 <- Y2.asr$asr_scr_internal_r
# Merge 0, 2 assessment data parent psychopathology (Self report)
Merged.asr_Baseline_Y2 <- merge(Baseline.asr, Y2.asr, by = "IID")
# Calculate correlation
correlation <- cor(Merged.asr_Baseline_Y2$asr_scr_internal_r_b, Merged.asr_Baseline_Y2$asr_scr_internal_r_2, use = "pairwise.complete.obs")
print(correlation)
## [1] 0.6915181
library(data.table)
# Keep subset for merge
variables_to_keep <- c("IID","asr_scr_internal_r_b","asr_scr_internal_r_2")
# Convert the data frame to a data.table
setDT(Merged.asr_Baseline_Y2)
# Subset the data table and create a new data table
Merged.asr_Baseline_Y2_subset <- Merged.asr_Baseline_Y2[, ..variables_to_keep]
# Create average asr variable
Merged.asr_Baseline_Y2_subset[, asr_int_ave := (asr_scr_internal_r_b + asr_scr_internal_r_2) / 2]
# Keep subset for acbl data
variables_to_keep <- c("IID","abcl_scr_prob_internal_r")
# Convert the data frame to a data.table
setDT(Y2.abcl)
# Subset the data table and create a new data table
Y2.abcl_subset <- Y2.abcl[, ..variables_to_keep]
# Merge asr abcl data
Merged.asr_abcl <- merge(Merged.asr_Baseline_Y2_subset, Y2.abcl_subset, by = "IID")
# Average together averaged parent internalizing self report with report on other parent
Merged.asr_abcl[, int_p_ave := (asr_int_ave + abcl_scr_prob_internal_r) / 2]
# Descriptive statistics
# Calculate correlation
correlation <- cor(Merged.asr_abcl$asr_int_ave, Merged.asr_abcl$abcl_scr_prob_internal_r, use = "pairwise.complete.obs")
print(correlation)
## [1] 0.4193454
famc <- read.csv("C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Data/abcd-data-release-5.1/core/culture-environment/ce_p_fes.csv", header = TRUE, sep = ",", dec = ".")
#View(famc)
describe(famc$fes_p_ss_fc)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 48733 2.43 1.94 2 2.25 1.48 0 9 9 0.7 -0.07 0.01
summary(famc$fes_p_ss_fc)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 2.000 2.429 4.000 9.000 418
#Create key for Identifiers:
famc.varlabels <- famc[1,]
#Remove first row of Identifiers so we are left with only the data:
famc.data <- famc[-1,]
#Rename subject ID
famc.data$IID <- famc.data[,"src_subject_id"]
#Create 0, 1, 2,3 assessment data sets
Baseline.famc<-famc.data[famc.data$eventname=="baseline_year_1_arm_1",]
Y1.famc<-famc.data[famc.data$eventname=="1_year_follow_up_y_arm_1",]
Y2.famc<-famc.data[famc.data$eventname=="2_year_follow_up_y_arm_1",]
Y3.famc<-famc.data[famc.data$eventname=="3_year_follow_up_y_arm_1",]
# Rename variables for merge
Baseline.famc$fes_p_ss_fc_b <- Baseline.famc$fes_p_ss_fc
Y1.famc$fes_p_ss_fc_1 <- Y1.famc$fes_p_ss_fc
Y2.famc$fes_p_ss_fc_2 <- Y2.famc$fes_p_ss_fc
Y3.famc$fes_p_ss_fc_3 <- Y3.famc$fes_p_ss_fc
# Merge all family conflict data sets together
library(data.table)
# List of datasets to merge
famc_datasets <- list(Baseline.famc, Y1.famc, Y2.famc, Y3.famc)
# Merge all data.tables using full outer join based on IID
famc_merged_data <- Reduce(function(x, y) merge(x, y, by = "IID", all = TRUE), famc_datasets)
## Warning in merge.data.frame(x, y, by = "IID", all = TRUE): column names
## 'src_subject_id.x', 'eventname.x', 'fam_enviro1_p.x', 'fam_enviro2r_p.x',
## 'fam_enviro3_p.x', 'fam_enviro4r_p.x', 'fam_enviro5_p.x', 'fam_enviro6_p.x',
## 'fam_enviro7r_p.x', 'fam_enviro8_p.x', 'fam_enviro9r_p.x',
## 'fam_enviro_select_language___1.x', 'fes_16r_p.x', 'fes_17_p.x', 'fes_19_p.x',
## 'fes_21_p.x', 'fes_22_p.x', 'fes_26_p.x', 'fes_27r_p.x', 'fes_29r_p.x',
## 'fes_31_p.x', 'fes_32_p.x', 'fes_36r_p.x', 'fes_37_p.x', 'fes_39_p.x',
## 'fes_41r_p.x', 'fes_42_p.x', 'fes_46r_p.x', 'fes_47_p.x', 'fes_49r_p.x',
## 'fes_51_p.x', 'fes_52r_p.x', 'fes_1_p.x', 'fes_56_p.x', 'fes_57r_p.x',
## 'fes_59_p.x', 'fes_61r_p.x', 'fes_62_p.x', 'fes_66_p.x', 'fes_67_p.x',
## 'fes_69_p.x', 'fes_71_p.x', 'fes_72r_p.x', 'fes_2r_p.x', 'fes_76r_p.x',
## 'fes_77_p.x', 'fes_79r_p.x', 'fes_81_p.x', 'fes_82_p.x', 'fes_86_p.x',
## 'fes_87r_p.x', 'fes_89_p.x', 'fes_6_p.x', 'fes_7r_p.x', 'fes_9_p.x',
## 'fes_11r_p.x', 'fes_12_p.x', 'fes_p_ss_fc.x', 'fes_p_ss_fc_na.x',
## 'fes_p_ss_fc_pr.x', 'fes_p_ss_fc_nm.x', 'fes_p_ss_fc_nt.x',
## 'fes_p_ss_exp_sum_nt.x', 'fes_p_ss_exp_sum_na.x', 'fes_p_ss_exp_sum_pr.x',
## 'fes_p_ss_int_cult_sum.x', 'fes_p_ss_int_cult_sum_nm.x',
## 'fes_p_ss_int_cult_sum_nt.x', 'fes_p_ss_int_cult_sum_na.x',
## 'fes_p_ss_int_cult_sum_pr.x', 'fes_p_ss_act_rec_sum.x',
## 'fes_p_ss_act_rec_sum_nm.x', 'fes_p_ss_act_rec_sum_nt.x',
## 'fes_p_ss_act_rec_sum_na.x', 'fes_p_ss_act_rec_sum_pr.x', 'fes_p_ss_org_sum.x',
## 'fes_p_ss_org_sum_nm.x', 'fes_p_ss_org_sum_nt.x', 'fes_p_ss_org_sum_na.x',
## 'fes_p_ss_org_sum_pr.x', 'fes_p_ss_cohesion_sum.x',
## 'fes_p_ss_cohesion_sum_nm.x', 'fes_p_ss_cohesion_sum_nt.x',
## 'fes_p_ss_cohesion_sum_na.x', 'fes_p_ss_cohesion_sum_pr.x',
## 'fes_p_ss_exp_sum.x', 'fes_p_ss_exp_sum_nm.x', 'src_subject_id.y',
## 'eventname.y', 'fam_enviro1_p.y', 'fam_enviro2r_p.y', 'fam_enviro3_p.y',
## 'fam_enviro4r_p.y', 'fam_enviro5_p.y', 'fam_enviro6_p.y', 'fam_enviro7r_p.y',
## 'fam_enviro8_p.y', 'fam_enviro9r_p.y', 'fam_enviro_select_language___1.y',
## 'fes_16r_p.y', 'fes_17_p.y', 'fes_19_p.y', 'fes_21_p.y', 'fes_22_p.y',
## 'fes_26_p.y', 'fes_27r_p.y', 'fes_29r_p.y', 'fes_31_p.y', 'fes_32_p.y',
## 'fes_36r_p.y', 'fes_37_p.y', 'fes_39_p.y', 'fes_41r_p.y', 'fes_42_p.y',
## 'fes_46r_p.y', 'fes_47_p.y', 'fes_49r_p.y', 'fes_51_p.y', 'fes_52r_p.y',
## 'fes_1_p.y', 'fes_56_p.y', 'fes_57r_p.y', 'fes_59_p.y', 'fes_61r_p.y',
## 'fes_62_p.y', 'fes_66_p.y', 'fes_67_p.y', 'fes_69_p.y', 'fes_71_p.y',
## 'fes_72r_p.y', 'fes_2r_p.y', 'fes_76r_p.y', 'fes_77_p.y', 'fes_79r_p.y',
## 'fes_81_p.y', 'fes_82_p.y', 'fes_86_p.y', 'fes_87r_p.y', 'fes_89_p.y',
## 'fes_6_p.y', 'fes_7r_p.y', 'fes_9_p.y', 'fes_11r_p.y', 'fes_12_p.y',
## 'fes_p_ss_fc.y', 'fes_p_ss_fc_na.y', 'fes_p_ss_fc_pr.y', 'fes_p_ss_fc_nm.y',
## 'fes_p_ss_fc_nt.y', 'fes_p_ss_exp_sum_nt.y', 'fes_p_ss_exp_sum_na.y',
## 'fes_p_ss_exp_sum_pr.y', 'fes_p_ss_int_cult_sum.y',
## 'fes_p_ss_int_cult_sum_nm.y', 'fes_p_ss_int_cult_sum_nt.y',
## 'fes_p_ss_int_cult_sum_na.y', 'fes_p_ss_int_cult_sum_pr.y',
## 'fes_p_ss_act_rec_sum.y', 'fes_p_ss_act_rec_sum_nm.y',
## 'fes_p_ss_act_rec_sum_nt.y', 'fes_p_ss_act_rec_sum_na.y',
## 'fes_p_ss_act_rec_sum_pr.y', 'fes_p_ss_org_sum.y', 'fes_p_ss_org_sum_nm.y',
## 'fes_p_ss_org_sum_nt.y', 'fes_p_ss_org_sum_na.y', 'fes_p_ss_org_sum_pr.y',
## 'fes_p_ss_cohesion_sum.y', 'fes_p_ss_cohesion_sum_nm.y',
## 'fes_p_ss_cohesion_sum_nt.y', 'fes_p_ss_cohesion_sum_na.y',
## 'fes_p_ss_cohesion_sum_pr.y', 'fes_p_ss_exp_sum.y', 'fes_p_ss_exp_sum_nm.y' are
## duplicated in the result
# Select the pub variables
famc_variables <- c("fes_p_ss_fc_b","fes_p_ss_fc_1", "fes_p_ss_fc_2", "fes_p_ss_fc_3")
# Subset the data for the selected pub variables
famc_data_corr <- famc_merged_data %>%
select(all_of(famc_variables))
# Compute the correlation matrix
cor_matrix <- cor(famc_data_corr, use = "pairwise.complete.obs")
cor_matrix
## fes_p_ss_fc_b fes_p_ss_fc_1 fes_p_ss_fc_2 fes_p_ss_fc_3
## fes_p_ss_fc_b 1.0000000 0.5759198 0.5667544 0.5339295
## fes_p_ss_fc_1 0.5759198 1.0000000 0.5981592 0.5789034
## fes_p_ss_fc_2 0.5667544 0.5981592 1.0000000 0.6635484
## fes_p_ss_fc_3 0.5339295 0.5789034 0.6635484 1.0000000
# Keep subset for merge
variables_to_keep <- c("IID","fes_p_ss_fc_b","fes_p_ss_fc_1", "fes_p_ss_fc_2", "fes_p_ss_fc_3")
# Convert the data frame to a data.table
setDT(famc_merged_data)
# Subset the data table and create a new data table
famc_merged_data_subset <- famc_merged_data[, ..variables_to_keep]
# Merge family conflict together across waves
famc_merged_data_subset[, famc_ave := rowSums(.SD, na.rm = TRUE) / 4, .SDcols = c("fes_p_ss_fc_b", "fes_p_ss_fc_1", "fes_p_ss_fc_2", "fes_p_ss_fc_3")]
# Keep subset for merge
variables_to_keep <- c("IID","famc_ave")
# Subset the data table and create a new data table
famc_merged_data_subset <- famc_merged_data_subset[, ..variables_to_keep]
# Child specific covariates
# Birth weight
birth <- read.csv("C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Data/abcd-data-release-5.1/core/physical-health/ph_p_dhx.csv", header = TRUE, sep = ",", dec = ".")
# Delete cases where 'eventname' is "4_year_follow_up_y_arm_1"
birth_baseline <- birth[birth$eventname != "4_year_follow_up_y_arm_1", ]
#Create key for Identifiers:
birth.varlabels <- birth_baseline[1,]
#Remove first row of Identifiers so we are left with only the data:
birth_baseline <- birth_baseline[-1,]
#Rename subject ID
birth_baseline$IID <- birth_baseline[,"src_subject_id"]
# Keep subset for merge
variables_to_keep <- c("IID","birth_weight_lbs","birth_weight_oz", "devhx_3_p", "devhx_9_alchohol_avg", "devhx_9_alcohol", "devhx_9_cigs_per_day", "devhx_9_tobacco", "devhx_9_marijuana_amt", "devhx_9_marijuana")
# Convert the data frame to a data.table
setDT(birth_baseline)
# Subset the data table and create a new data table
birth.data_subset <- birth_baseline[, ..variables_to_keep]
# Convert ounces to pounds (1 lb = 16 oz) and create birth.data weight variable
birth.data_subset$bw_lbs <- birth.data_subset$birth_weight_lbs + birth.data_subset$birth_weight_oz / 16
describe(birth.data_subset$bw_lbs)
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 10622 7.02 1.47 7.19 7.07 1.39 1.81 14.75 12.94 -0.29 0.32
## se
## X1 0.01
hist(birth.data_subset$bw_lbs,
main = "Histogram of birth.data Weight", # Title of the histogram
xlab = "Value", # Label for the x-axis
ylab = "Frequency", # Label for the y-axis
col = "blue", # Color of the bars
border = "black", # Color of the bar borders
breaks = 5)
# Maternal age
birth.data_subset$matage <- birth.data_subset$devhx_3_p
describe(birth.data_subset$matage)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 11603 29.39 6.27 30 29.39 5.93 13 60 47 0.02 -0.42 0.06
hist(birth.data_subset$matage,
main = "Histogram of Maternal Age", # Title of the histogram
xlab = "Value", # Label for the x-axis
ylab = "Frequency", # Label for the y-axis
col = "blue", # Color of the bars
border = "black", # Color of the bar borders
breaks = 10)
# Alcohol use
birth.data_subset$devhx_9_alchohol_avg <- ifelse(birth.data_subset$devhx_9_alcohol == 0, 0,
ifelse(birth.data_subset$devhx_9_alcohol == 999, NA,
no = birth.data_subset$devhx_9_alchohol_avg))
birth.data_subset$matalc_ave <- birth.data_subset$devhx_9_alchohol_avg
describe(birth.data_subset$matalc_ave)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 11506 0.05 1.01 0 0 0 0 60 60 42.79 2171 0.01
freq_devhx_9_alchohol_avg <- table(birth.data_subset$devhx_9_alchohol_avg)
print(freq_devhx_9_alchohol_avg)
##
## 0 0.025 0.045 0.05 0.1 0.2 0.25 0.5 0.6 1 2 3 4
## 11316 1 1 1 4 1 5 9 1 90 32 16 7
## 5 6 7 8 10 15 18 20 21 50 60
## 2 2 4 1 5 1 1 1 2 2 1
hist(birth.data_subset$matalc_ave,
main = "Histogram of Average Drinks Per Week", # Title of the histogram
xlab = "Value", # Label for the x-axis
ylab = "Frequency", # Label for the y-axis
col = "blue", # Color of the bars
border = "black", # Color of the bar borders
breaks = 10)
# Cigarette use
# Recode devhx_9_cigs_per_day
birth.data_subset$devhx_9_cigs_per_day <- ifelse(birth.data_subset$devhx_9_tobacco == 0, 0,
ifelse(birth.data_subset$devhx_9_tobacco == 999, NA,
no = birth.data_subset$devhx_9_cigs_per_day))
birth.data_subset$matcig_ave <- birth.data_subset$devhx_9_cigs_per_day
describe(birth.data_subset$matcig_ave)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 11491 0.33 1.97 0 0 0 0 30 30 7.85 72.71 0.02
hist(birth.data_subset$matcig_ave,
main = "Histogram of Average Cigarette Use Per Day", # Title of the histogram
xlab = "Value", # Label for the x-axis
ylab = "Frequency", # Label for the y-axis
col = "blue", # Color of the bars
border = "black", # Color of the bar borders
breaks = 10)
# Marijuana
birth.data_subset$devhx_9_marijuana_amt <- ifelse(birth.data_subset$devhx_9_marijuana == 0, 0,
ifelse(birth.data_subset$devhx_9_marijuana == 999, NA,
no = birth.data_subset$devhx_9_marijuana_amt))
birth.data_subset$matmar_ave <- birth.data_subset$devhx_9_marijuana_amt
describe(birth.data_subset$matmar_ave)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 11496 0.03 0.26 0 0 0 0 8 8 13.69 237.81 0
hist(birth.data_subset$matmar_ave,
main = "Histogram of Average Marijuana Use Per Day", # Title of the histogram
xlab = "Value", # Label for the x-axis
ylab = "Frequency", # Label for the y-axis
col = "blue", # Color of the bars
border = "black", # Color of the bar borders
breaks = 10)
# General socioeconomic status latent factor
ses <- read.csv("C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Data/abcd-data-release-5.1/core/abcd-general/abcd_y_lf.csv", header = TRUE, sep = ",", dec = ".")
#Create key for Identifiers:
ses.varlabels <- ses[1,]
#Remove first row of Identifiers so we are left with only the data:
ses.data <- ses[-1,]
#Rename subject ID
ses.data$IID <- ses.data[,"src_subject_id"]
# Keep subset for merge
variables_to_keep <- c("IID","latent_factor_ss_general_ses")
# Convert the data frame to a data.table
setDT(ses.data)
# Subset the data table and create a new data table
ses.data_subset <- ses.data[, ..variables_to_keep]
ses.data_subset$ses_lt <- ses.data_subset$latent_factor_ss_general_ses
describe(ses.data_subset$ses_lt)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 8150 0 0.94 0.26 0.12 0.76 -3.94 1.69 5.62 -1.22 1.33 0.01
hist(ses.data_subset$ses_lt,
main = "Histogram of SES Latent Factor Score", # Title of the histogram
xlab = "Value", # Label for the x-axis
ylab = "Frequency", # Label for the y-axis
col = "blue", # Color of the bars
border = "black", # Color of the bar borders
breaks = 10)
################### Merge all covariate data sets together ####################
library(data.table)
# List of datasets to merge
cov_datasets <- list(Merged.asr_abcl, famc_merged_data_subset, birth.data_subset, ses.data_subset)
# Merge all data.tables using full outer join based on IID
cov_merged_data <- Reduce(function(x, y) merge(x, y, by = "IID", all = TRUE), cov_datasets)
# Merge all constructed datasets for each phenotype together
library(data.table)
# Drop redundant age variables before merge
library(dplyr)
# List of variables to drop
variables_to_drop <- c("age_Baseline", "age_Y1", "age_Y2", "age_Y3")
# Drop the specified variables from the wchr dataset
wchr_merged_data <- wchr_merged_data %>%
select(-all_of(variables_to_drop))
# Drop the specified variables from the puberty dataset
pub_merged_data <- pub_merged_data %>%
select(-all_of(variables_to_drop))
# List of datasets to merge
all_datasets <- list(int_merged_data, wchr_merged_data, pub_merged_data, cov_merged_data, Baseline.demo)
# Merge all data.tables on IID
all_merged <- Reduce(function(x, y) merge(x, y, by = "IID", all = TRUE), all_datasets)
# Find duplicate IDs
# duplicate_ids <- all_merged$IID[duplicated(all_merged$IID)]
# Display the duplicate IDs
# print(duplicate_ids)
# install.packages("Hmisc")
# library("Hmisc")
library("psych")
# Examining cross phenotype associations
# Select the variables to describe
variables_to_describe <- c("age_Baseline", "int_Baseline", "intt_Baseline",
"age_Y1", "int_Y1", "intt_Y1",
"age_Y2", "int_Y2", "intt_Y2",
"age_Y3", "int_Y3", "intt_Y3",
"age_Baseline", "wchr_Baseline", "wchrt_Baseline",
"age_Y1", "wchr_Y1", "wchrt_Y1",
"age_Y2", "wchr_Y2", "wchrt_Y2",
"age_Y3", "wchr_Y3", "wchrt_Y3",
"age_Baseline", "pub_Baseline", "pubt_Baseline",
"age_Y1", "pub_Y1", "pubt_Y1",
"age_Y2", "pub_Y2", "pubt_Y2",
"age_Y3", "pub_Y3", "pubt_Y3",
"int_p_ave", "famc_ave", "bw_lbs",
"matage", "matalc_ave", "matcig_ave",
"matmar_ave", "ses_lt")
# Convert data.table to data frame
data_frame <- as.data.frame(all_merged)
# Use the describe() function to obtain descriptive statistics
variable_descriptions <- describe(data_frame[variables_to_describe])
# Print the descriptive statistics
print(variable_descriptions)
## vars n mean sd median trimmed mad min max range
## age_Baseline 1 10360 118.97 7.49 119.00 118.87 10.38 107.00 133.00 26.00
## int_Baseline 2 10358 1.03 1.70 0.00 0.64 0.00 0.00 15.00 15.00
## intt_Baseline 3 10355 0.00 1.70 -0.91 -0.37 0.34 -1.16 13.91 15.08
## age_Y1 4 9784 131.08 7.72 131.00 130.99 10.38 117.00 149.00 32.00
## int_Y1 5 9783 1.11 1.78 0.00 0.71 0.00 0.00 14.00 14.00
## intt_Y1 6 9780 0.00 1.77 -0.99 -0.38 0.36 -1.30 12.91 14.21
## age_Y2 7 9523 144.28 8.01 144.00 144.18 10.38 127.00 168.00 41.00
## int_Y2 8 9522 1.23 1.96 0.00 0.79 0.00 0.00 16.00 16.00
## intt_Y2 9 9519 0.00 1.96 -1.02 -0.42 0.63 -1.70 15.03 16.73
## age_Y3 10 8825 154.93 7.77 155.00 154.83 10.38 137.00 175.00 38.00
## int_Y3 11 8821 1.44 2.15 1.00 0.97 1.48 0.00 15.00 15.00
## intt_Y3 12 8819 0.00 2.15 -0.67 -0.44 1.18 -1.82 13.69 15.52
## age_Baseline.1 13 10360 118.97 7.49 119.00 118.87 10.38 107.00 133.00 26.00
## wchr_Baseline 14 10345 0.48 0.07 0.47 0.47 0.06 0.24 1.37 1.13
## wchrt_Baseline 15 10342 0.00 0.07 -0.01 -0.01 0.06 -0.24 0.89 1.13
## age_Y1.1 16 9784 131.08 7.72 131.00 130.99 10.38 117.00 149.00 32.00
## wchr_Y1 17 9714 0.48 0.08 0.47 0.47 0.06 0.27 1.47 1.21
## wchrt_Y1 18 9711 0.00 0.08 -0.02 -0.01 0.06 -0.21 1.00 1.21
## age_Y2.1 19 9523 144.28 8.01 144.00 144.18 10.38 127.00 168.00 41.00
## wchr_Y2 20 7919 0.48 0.08 0.46 0.47 0.07 0.26 1.41 1.15
## wchrt_Y2 21 7917 0.00 0.08 -0.02 -0.01 0.07 -0.22 0.92 1.14
## age_Y3.1 22 8825 154.93 7.77 155.00 154.83 10.38 137.00 175.00 38.00
## wchr_Y3 23 1755 0.49 0.09 0.47 0.48 0.07 0.34 1.26 0.92
## wchrt_Y3 24 1754 0.00 0.08 -0.02 -0.01 0.07 -0.15 0.77 0.92
## age_Baseline.2 25 10360 118.97 7.49 119.00 118.87 10.38 107.00 133.00 26.00
## pub_Baseline 26 8397 2.08 0.83 2.00 2.06 1.48 1.00 5.00 4.00
## pubt_Baseline 27 8363 0.00 0.81 0.05 -0.01 1.05 -1.61 3.08 4.69
## age_Y1.2 28 9784 131.08 7.72 131.00 130.99 10.38 117.00 149.00 32.00
## pub_Y1 29 4247 2.27 0.90 2.00 2.25 1.48 1.00 5.00 4.00
## pubt_Y1 30 4224 0.00 0.80 0.04 0.00 0.95 -2.12 3.08 5.21
## age_Y2.2 31 9523 144.28 8.01 144.00 144.18 10.38 127.00 168.00 41.00
## pub_Y2 32 9067 2.69 0.97 3.00 2.73 1.48 1.00 5.00 4.00
## pubt_Y2 33 9064 0.00 0.77 0.04 0.02 0.75 -2.84 3.02 5.86
## age_Y3.2 34 8825 154.93 7.77 155.00 154.83 10.38 137.00 175.00 38.00
## pub_Y3 35 8595 3.12 0.93 3.00 3.20 1.48 1.00 5.00 4.00
## pubt_Y3 36 8592 0.00 0.74 0.11 0.03 0.69 -2.94 2.53 5.47
## int_p_ave 37 9202 7.71 6.17 6.00 6.85 5.19 0.00 48.50 48.50
## famc_ave 38 11868 2.26 1.58 2.00 2.12 1.48 0.00 8.75 8.75
## bw_lbs 39 10622 7.02 1.47 7.19 7.07 1.39 1.81 14.75 12.94
## matage 40 11603 29.39 6.27 30.00 29.39 5.93 13.00 60.00 47.00
## matalc_ave 41 11506 0.05 1.01 0.00 0.00 0.00 0.00 60.00 60.00
## matcig_ave 42 11491 0.33 1.97 0.00 0.00 0.00 0.00 30.00 30.00
## matmar_ave 43 11496 0.03 0.26 0.00 0.00 0.00 0.00 8.00 8.00
## ses_lt 44 8150 0.00 0.94 0.26 0.12 0.76 -3.94 1.69 5.62
## skew kurtosis se
## age_Baseline 0.07 -1.26 0.07
## int_Baseline 2.52 8.16 0.02
## intt_Baseline 2.51 8.15 0.02
## age_Y1 0.07 -1.18 0.08
## int_Y1 2.42 7.28 0.02
## intt_Y1 2.42 7.26 0.02
## age_Y2 0.11 -0.93 0.08
## int_Y2 2.38 7.12 0.02
## intt_Y2 2.36 7.07 0.02
## age_Y3 0.09 -1.02 0.08
## int_Y3 2.14 5.47 0.02
## intt_Y3 2.12 5.38 0.02
## age_Baseline.1 0.07 -1.26 0.07
## wchr_Baseline 1.31 4.92 0.00
## wchrt_Baseline 1.31 4.92 0.00
## age_Y1.1 0.07 -1.18 0.08
## wchr_Y1 2.05 13.96 0.00
## wchrt_Y1 2.05 13.98 0.00
## age_Y2.1 0.11 -0.93 0.08
## wchr_Y2 1.52 6.84 0.00
## wchrt_Y2 1.52 6.77 0.00
## age_Y3.1 0.09 -1.02 0.08
## wchr_Y3 1.72 7.24 0.00
## wchrt_Y3 1.72 7.30 0.00
## age_Baseline.2 0.07 -1.26 0.07
## pub_Baseline 0.19 -0.75 0.01
## pubt_Baseline 0.13 -0.58 0.01
## age_Y1.2 0.07 -1.18 0.08
## pub_Y1 0.09 -0.76 0.01
## pubt_Y1 -0.04 -0.38 0.01
## age_Y2.2 0.11 -0.93 0.08
## pub_Y2 -0.17 -0.73 0.01
## pubt_Y2 -0.28 0.11 0.01
## age_Y3.2 0.09 -1.02 0.08
## pub_Y3 -0.52 -0.27 0.01
## pubt_Y3 -0.46 0.55 0.01
## int_p_ave 1.40 2.38 0.06
## famc_ave 0.79 0.28 0.01
## bw_lbs -0.29 0.32 0.01
## matage 0.02 -0.42 0.06
## matalc_ave 42.79 2171.00 0.01
## matcig_ave 7.85 72.71 0.02
## matmar_ave 13.69 237.81 0.00
## ses_lt -1.22 1.33 0.01
# Select the variables
all_variables <- c("int_Baseline", "int_Y1", "int_Y2", "int_Y3","int_Baseline", "wchr_Baseline", "wchr_Y1", "wchr_Y2", "wchr_Y3","pubt_Baseline", "pub_Y1", "pub_Y2", "pub_Y3")
# Select the variables for age regressed
allt_variables <- c("intt_Baseline", "intt_Y1", "intt_Y2", "intt_Y3","intt_Baseline", "wchrt_Baseline", "wchrt_Y1", "wchrt_Y2", "wchrt_Y3","pubt_Baseline", "pubt_Y1", "pubt_Y2", "pubt_Y3")
# Subset the data for the selected variables
all_data <- data_frame %>%
select(all_of(all_variables))
# Subset the data for the selected t variables
allt_data <- data_frame %>%
select(all_of(allt_variables))
# Compute the correlation matrix for raw vars
cor_matrix <- cor(all_data, use = "pairwise.complete.obs")
cor_matrix
## int_Baseline int_Y1 int_Y2 int_Y3 wchr_Baseline
## int_Baseline 1.000000000 0.630956838 0.53983486 0.46201264 0.09421033
## int_Y1 0.630956838 1.000000000 0.61654564 0.54822161 0.10997044
## int_Y2 0.539834859 0.616545644 1.00000000 0.63135150 0.08641891
## int_Y3 0.462012636 0.548221610 0.63135150 1.00000000 0.08709353
## wchr_Baseline 0.094210334 0.109970439 0.08641891 0.08709353 1.00000000
## wchr_Y1 0.082155429 0.097856727 0.07732259 0.08014528 0.71680624
## wchr_Y2 0.092845659 0.104111508 0.09641904 0.09949183 0.69831350
## wchr_Y3 0.109665239 0.096286181 0.10456406 0.11794590 0.65697357
## pubt_Baseline 0.045649550 0.041600401 0.04626853 0.05403143 0.11263871
## pub_Y1 -0.006522673 0.001727880 0.04866219 0.08002086 0.09795445
## pub_Y2 -0.007341246 0.013483119 0.05397715 0.07161682 0.10864425
## pub_Y3 -0.019332393 0.002501385 0.03781863 0.08179382 0.09660333
## wchr_Y1 wchr_Y2 wchr_Y3 pubt_Baseline pub_Y1
## int_Baseline 0.08215543 0.09284566 0.10966524 0.04564955 -0.006522673
## int_Y1 0.09785673 0.10411151 0.09628618 0.04160040 0.001727880
## int_Y2 0.07732259 0.09641904 0.10456406 0.04626853 0.048662189
## int_Y3 0.08014528 0.09949183 0.11794590 0.05403143 0.080020859
## wchr_Baseline 0.71680624 0.69831350 0.65697357 0.11263871 0.097954455
## wchr_Y1 1.00000000 0.71687773 0.71355018 0.12288615 0.090946935
## wchr_Y2 0.71687773 1.00000000 0.78890862 0.11102490 0.103963494
## wchr_Y3 0.71355018 0.78890862 1.00000000 0.12089002 0.130695829
## pubt_Baseline 0.12288615 0.11102490 0.12089002 1.00000000 0.335130363
## pub_Y1 0.09094694 0.10396349 0.13069583 0.33513036 1.000000000
## pub_Y2 0.07793357 0.07602554 0.12102640 0.21109658 0.619215464
## pub_Y3 0.06772496 0.05964220 0.10013681 0.14508693 0.520463625
## pub_Y2 pub_Y3
## int_Baseline -0.007341246 -0.019332393
## int_Y1 0.013483119 0.002501385
## int_Y2 0.053977146 0.037818628
## int_Y3 0.071616821 0.081793822
## wchr_Baseline 0.108644246 0.096603333
## wchr_Y1 0.077933573 0.067724956
## wchr_Y2 0.076025543 0.059642205
## wchr_Y3 0.121026397 0.100136805
## pubt_Baseline 0.211096582 0.145086928
## pub_Y1 0.619215464 0.520463625
## pub_Y2 1.000000000 0.689156941
## pub_Y3 0.689156941 1.000000000
# Compute the correlation matrix for age regressed vars
cor_matrix_t <- cor(allt_data, use = "pairwise.complete.obs")
cor_matrix_t
## intt_Baseline intt_Y1 intt_Y2 intt_Y3 wchrt_Baseline
## intt_Baseline 1.00000000 0.63064547 0.54124862 0.46623642 0.09420546
## intt_Y1 0.63064547 1.00000000 0.61664321 0.54982975 0.11025942
## intt_Y2 0.54124862 0.61664321 1.00000000 0.63225262 0.08747187
## intt_Y3 0.46623642 0.54982975 0.63225262 1.00000000 0.08760491
## wchrt_Baseline 0.09420546 0.11025942 0.08747187 0.08760491 1.00000000
## wchrt_Y1 0.08176716 0.09823265 0.07972661 0.08159648 0.71684106
## wchrt_Y2 0.09178623 0.10458623 0.09745399 0.10113100 0.69801814
## wchrt_Y3 0.10955623 0.09625793 0.10486864 0.11529913 0.65824640
## pubt_Baseline 0.04578610 0.04161121 0.04700142 0.05528502 0.11266131
## pubt_Y1 0.02991298 0.02365393 0.05320324 0.07119100 0.12027224
## pubt_Y2 0.02408069 0.02140225 0.04578905 0.05380737 0.14853834
## pubt_Y3 0.01959947 0.01548506 0.03790738 0.06451921 0.13071109
## wchrt_Y1 wchrt_Y2 wchrt_Y3 pubt_Baseline pubt_Y1
## intt_Baseline 0.08176716 0.09178623 0.10955623 0.04578610 0.02991298
## intt_Y1 0.09823265 0.10458623 0.09625793 0.04161121 0.02365393
## intt_Y2 0.07972661 0.09745399 0.10486864 0.04700142 0.05320324
## intt_Y3 0.08159648 0.10113100 0.11529913 0.05528502 0.07119100
## wchrt_Baseline 0.71684106 0.69801814 0.65824640 0.11266131 0.12027224
## wchrt_Y1 1.00000000 0.71660044 0.71515631 0.12299839 0.12462692
## wchrt_Y2 0.71660044 1.00000000 0.78839622 0.11072695 0.13146828
## wchrt_Y3 0.71515631 0.78839622 1.00000000 0.12095920 0.14146887
## pubt_Baseline 0.12299839 0.11072695 0.12095920 1.00000000 0.38587966
## pubt_Y1 0.12462692 0.13146828 0.14146887 0.38587966 1.00000000
## pubt_Y2 0.12407488 0.11678282 0.14725275 0.27067965 0.49622410
## pubt_Y3 0.10888941 0.10147091 0.12343566 0.18965426 0.36424327
## pubt_Y2 pubt_Y3
## intt_Baseline 0.02408069 0.01959947
## intt_Y1 0.02140225 0.01548506
## intt_Y2 0.04578905 0.03790738
## intt_Y3 0.05380737 0.06451921
## wchrt_Baseline 0.14853834 0.13071109
## wchrt_Y1 0.12407488 0.10888941
## wchrt_Y2 0.11678282 0.10147091
## wchrt_Y3 0.14725275 0.12343566
## pubt_Baseline 0.27067965 0.18965426
## pubt_Y1 0.49622410 0.36424327
## pubt_Y2 1.00000000 0.51949657
## pubt_Y3 0.51949657 1.00000000
# Create a ggheatmap
ggheatmap <- ggplot(melt(cor_matrix), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 3) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# Create a ggheatmap for age regressed variables
ggheatmap <- ggplot(melt(cor_matrix_t), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 3) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# Correlation matrix with covariates
# Select the variables for age regressed and covariates
allt_cov_variables <- c("intt_Baseline", "intt_Y1", "intt_Y2", "intt_Y3","intt_Baseline", "wchrt_Baseline", "wchrt_Y1", "wchrt_Y2", "wchrt_Y3","pubt_Baseline", "pubt_Y1", "pubt_Y2", "pubt_Y3", "int_p_ave", "famc_ave", "bw_lbs", "matage", "matalc_ave", "matcig_ave", "matmar_ave", "ses_lt")
# Subset the data for the selected t variables
allt_cov_data <- data_frame %>%
select(all_of(allt_cov_variables))
# Compute the correlation matrix for age regressed vars + covariates
cor_matrix_cov_t <- cor(allt_cov_data, use = "pairwise.complete.obs")
cor_matrix_cov_t
## intt_Baseline intt_Y1 intt_Y2 intt_Y3
## intt_Baseline 1.00000000 0.630645473 0.54124862 0.46623642
## intt_Y1 0.63064547 1.000000000 0.61664321 0.54982975
## intt_Y2 0.54124862 0.616643208 1.00000000 0.63225262
## intt_Y3 0.46623642 0.549829749 0.63225262 1.00000000
## wchrt_Baseline 0.09420546 0.110259417 0.08747187 0.08760491
## wchrt_Y1 0.08176716 0.098232649 0.07972661 0.08159648
## wchrt_Y2 0.09178623 0.104586229 0.09745399 0.10113100
## wchrt_Y3 0.10955623 0.096257927 0.10486864 0.11529913
## pubt_Baseline 0.04578610 0.041611215 0.04700142 0.05528502
## pubt_Y1 0.02991298 0.023653930 0.05320324 0.07119100
## pubt_Y2 0.02408069 0.021402251 0.04578905 0.05380737
## pubt_Y3 0.01959947 0.015485063 0.03790738 0.06451921
## int_p_ave 0.37275936 0.375394922 0.42555967 0.36468496
## famc_ave 0.16332523 0.163910352 0.18646043 0.19799013
## bw_lbs 0.00239957 0.020540328 0.02014186 0.04266874
## matage -0.09619319 -0.064933563 -0.04302733 -0.03271720
## matalc_ave 0.02607338 0.005490829 0.03071013 0.02631768
## matcig_ave 0.06180368 0.079261679 0.06899566 0.07294876
## matmar_ave 0.04767395 0.038943782 0.05110678 0.05030890
## ses_lt -0.24253285 -0.204770990 -0.17953514 -0.15227664
## wchrt_Baseline wchrt_Y1 wchrt_Y2 wchrt_Y3
## intt_Baseline 0.094205455 0.0817671618 0.091786228 0.10955623
## intt_Y1 0.110259417 0.0982326486 0.104586229 0.09625793
## intt_Y2 0.087471867 0.0797266136 0.097453985 0.10486864
## intt_Y3 0.087604910 0.0815964845 0.101130997 0.11529913
## wchrt_Baseline 1.000000000 0.7168410617 0.698018144 0.65824640
## wchrt_Y1 0.716841062 1.0000000000 0.716600444 0.71515631
## wchrt_Y2 0.698018144 0.7166004442 1.000000000 0.78839622
## wchrt_Y3 0.658246397 0.7151563079 0.788396218 1.00000000
## pubt_Baseline 0.112661307 0.1229983865 0.110726948 0.12095920
## pubt_Y1 0.120272245 0.1246269217 0.131468279 0.14146887
## pubt_Y2 0.148538336 0.1240748799 0.116782824 0.14725275
## pubt_Y3 0.130711091 0.1088894123 0.101470910 0.12343566
## int_p_ave 0.040478356 0.0581244791 0.051730479 0.04544840
## famc_ave -0.011379753 -0.0002494606 0.005091372 0.02619326
## bw_lbs 0.076058866 0.0674241045 0.041641456 0.08870230
## matage -0.081518900 -0.0898311275 -0.103686013 -0.13210681
## matalc_ave 0.008659337 0.0024405805 0.004639121 -0.02753863
## matcig_ave 0.066930732 0.0597793669 0.073617674 0.15610422
## matmar_ave 0.038093638 0.0464731525 0.049507906 0.01815518
## ses_lt -0.243731430 -0.2249491987 -0.273176660 -0.26153002
## pubt_Baseline pubt_Y1 pubt_Y2 pubt_Y3 int_p_ave
## intt_Baseline 0.045786104 0.02991298 0.02408069 0.0195994650 0.37275936
## intt_Y1 0.041611215 0.02365393 0.02140225 0.0154850631 0.37539492
## intt_Y2 0.047001417 0.05320324 0.04578905 0.0379073753 0.42555967
## intt_Y3 0.055285024 0.07119100 0.05380737 0.0645192122 0.36468496
## wchrt_Baseline 0.112661307 0.12027224 0.14853834 0.1307110912 0.04047836
## wchrt_Y1 0.122998386 0.12462692 0.12407488 0.1088894123 0.05812448
## wchrt_Y2 0.110726948 0.13146828 0.11678282 0.1014709100 0.05173048
## wchrt_Y3 0.120959201 0.14146887 0.14725275 0.1234356635 0.04544840
## pubt_Baseline 1.000000000 0.38587966 0.27067965 0.1896542588 0.03599774
## pubt_Y1 0.385879660 1.00000000 0.49622410 0.3642432680 0.02901371
## pubt_Y2 0.270679647 0.49622410 1.00000000 0.5194965686 0.03884291
## pubt_Y3 0.189654259 0.36424327 0.51949657 1.0000000000 0.03526137
## int_p_ave 0.035997741 0.02901371 0.03884291 0.0352613684 1.00000000
## famc_ave -0.009152256 -0.02155805 -0.01616332 -0.0305782523 0.32783349
## bw_lbs -0.015409250 0.00589589 -0.02708632 0.0034639507 0.01603960
## matage -0.107375368 -0.07173358 -0.09377695 -0.0789720115 -0.06562474
## matalc_ave -0.006473348 0.01383175 0.01879134 0.0004869273 0.04080926
## matcig_ave 0.030274659 0.04971785 0.03031331 0.0118858166 0.10657556
## matmar_ave 0.027808883 0.05044946 0.02405944 0.0081518004 0.06782937
## ses_lt -0.177075055 -0.19264180 -0.17437762 -0.1453937375 -0.31987348
## famc_ave bw_lbs matage matalc_ave
## intt_Baseline 0.1633252316 0.002399570 -0.096193191 0.0260733750
## intt_Y1 0.1639103521 0.020540328 -0.064933563 0.0054908293
## intt_Y2 0.1864604328 0.020141862 -0.043027330 0.0307101310
## intt_Y3 0.1979901286 0.042668742 -0.032717199 0.0263176845
## wchrt_Baseline -0.0113797530 0.076058866 -0.081518900 0.0086593372
## wchrt_Y1 -0.0002494606 0.067424104 -0.089831127 0.0024405805
## wchrt_Y2 0.0050913717 0.041641456 -0.103686013 0.0046391205
## wchrt_Y3 0.0261932587 0.088702296 -0.132106813 -0.0275386332
## pubt_Baseline -0.0091522555 -0.015409250 -0.107375368 -0.0064733477
## pubt_Y1 -0.0215580542 0.005895890 -0.071733583 0.0138317451
## pubt_Y2 -0.0161633150 -0.027086321 -0.093776954 0.0187913431
## pubt_Y3 -0.0305782523 0.003463951 -0.078972012 0.0004869273
## int_p_ave 0.3278334852 0.016039604 -0.065624737 0.0408092630
## famc_ave 1.0000000000 -0.004026514 0.019965628 0.0256368853
## bw_lbs -0.0040265138 1.000000000 -0.004664972 -0.0109385931
## matage 0.0199656281 -0.004664972 1.000000000 0.0220110992
## matalc_ave 0.0256368853 -0.010938593 0.022011099 1.0000000000
## matcig_ave 0.0262981455 -0.048991436 -0.071868552 0.1898145027
## matmar_ave 0.0144338679 -0.006957648 -0.079615410 0.0273110257
## ses_lt -0.1113415610 0.060106035 0.503106903 -0.0173600688
## matcig_ave matmar_ave ses_lt
## intt_Baseline 0.06180368 0.047673953 -0.24253285
## intt_Y1 0.07926168 0.038943782 -0.20477099
## intt_Y2 0.06899566 0.051106781 -0.17953514
## intt_Y3 0.07294876 0.050308902 -0.15227664
## wchrt_Baseline 0.06693073 0.038093638 -0.24373143
## wchrt_Y1 0.05977937 0.046473153 -0.22494920
## wchrt_Y2 0.07361767 0.049507906 -0.27317666
## wchrt_Y3 0.15610422 0.018155182 -0.26153002
## pubt_Baseline 0.03027466 0.027808883 -0.17707505
## pubt_Y1 0.04971785 0.050449456 -0.19264180
## pubt_Y2 0.03031331 0.024059436 -0.17437762
## pubt_Y3 0.01188582 0.008151800 -0.14539374
## int_p_ave 0.10657556 0.067829366 -0.31987348
## famc_ave 0.02629815 0.014433868 -0.11134156
## bw_lbs -0.04899144 -0.006957648 0.06010603
## matage -0.07186855 -0.079615410 0.50310690
## matalc_ave 0.18981450 0.027311026 -0.01736007
## matcig_ave 1.00000000 0.117620621 -0.21936176
## matmar_ave 0.11762062 1.000000000 -0.15323254
## ses_lt -0.21936176 -0.153232535 1.00000000
# Create a ggheatmap for age regressed variables + covariates
ggheatmap <- ggplot(melt(cor_matrix_cov_t), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 2) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# subset data to just White participants
subset_all_merged <- all_merged[all_merged$race3 == 1, ]
# Examining cross phenotype associations
# Select the variables to describe
variables_to_describe <- c("age_Baseline", "int_Baseline", "intt_Baseline",
"age_Y1", "int_Y1", "intt_Y1",
"age_Y2", "int_Y2", "intt_Y2",
"age_Y3", "int_Y3", "intt_Y3",
"age_Baseline", "wchr_Baseline", "wchrt_Baseline",
"age_Y1", "wchr_Y1", "wchrt_Y1",
"age_Y2", "wchr_Y2", "wchrt_Y2",
"age_Y3", "wchr_Y3", "wchrt_Y3",
"age_Baseline", "pub_Baseline", "pubt_Baseline",
"age_Y1", "pub_Y1", "pubt_Y1",
"age_Y2", "pub_Y2", "pubt_Y2",
"age_Y3", "pub_Y3", "pubt_Y3",
"int_p_ave", "famc_ave", "bw_lbs",
"matage", "matalc_ave", "matcig_ave",
"matmar_ave", "ses_lt")
# Convert data.table to data frame
data_frame <- as.data.frame(subset_all_merged)
# Use the describe() function to obtain descriptive statistics
variable_descriptions <- describe(data_frame[variables_to_describe])
# Print the descriptive statistics
print(variable_descriptions)
## vars n mean sd median trimmed mad min max range
## age_Baseline 1 6170 119.17 7.52 119.00 119.09 10.38 107.00 132.00 25.00
## int_Baseline 2 6168 0.96 1.61 0.00 0.60 0.00 0.00 14.00 14.00
## intt_Baseline 3 6167 -0.06 1.61 -0.92 -0.41 0.33 -1.16 12.92 14.08
## age_Y1 4 5975 131.34 7.75 131.00 131.27 10.38 117.00 149.00 32.00
## int_Y1 5 5975 1.06 1.72 0.00 0.67 0.00 0.00 14.00 14.00
## intt_Y1 6 5974 -0.05 1.72 -0.99 -0.42 0.35 -1.28 12.91 14.19
## age_Y2 7 5831 144.48 8.03 144.00 144.39 10.38 127.00 168.00 41.00
## int_Y2 8 5830 1.21 1.92 0.00 0.78 0.00 0.00 15.00 15.00
## intt_Y2 9 5829 -0.03 1.91 -1.03 -0.43 0.62 -1.70 13.87 15.56
## age_Y3 10 5487 155.16 7.76 155.00 155.07 8.90 137.00 175.00 38.00
## int_Y3 11 5484 1.46 2.15 1.00 0.99 1.48 0.00 15.00 15.00
## intt_Y3 12 5484 0.01 2.14 -0.63 -0.42 1.25 -1.82 13.69 15.52
## age_Baseline.1 13 6170 119.17 7.52 119.00 119.09 10.38 107.00 132.00 25.00
## wchr_Baseline 14 6161 0.47 0.06 0.45 0.46 0.05 0.30 0.81 0.51
## wchrt_Baseline 15 6160 -0.01 0.06 -0.03 -0.02 0.05 -0.18 0.33 0.51
## age_Y1.1 16 5975 131.34 7.75 131.00 131.27 10.38 117.00 149.00 32.00
## wchr_Y1 17 5942 0.47 0.07 0.46 0.46 0.05 0.27 1.42 1.16
## wchrt_Y1 18 5941 -0.01 0.07 -0.03 -0.02 0.05 -0.21 0.94 1.15
## age_Y2.1 19 5831 144.48 8.03 144.00 144.39 10.38 127.00 168.00 41.00
## wchr_Y2 20 5018 0.47 0.07 0.45 0.46 0.06 0.26 1.39 1.13
## wchrt_Y2 21 5017 -0.01 0.07 -0.03 -0.02 0.06 -0.22 0.90 1.12
## age_Y3.1 22 5487 155.16 7.76 155.00 155.07 8.90 137.00 175.00 38.00
## wchr_Y3 23 1114 0.47 0.07 0.46 0.47 0.06 0.34 1.26 0.92
## wchrt_Y3 24 1114 -0.02 0.07 -0.03 -0.02 0.06 -0.15 0.77 0.92
## age_Baseline.2 25 6170 119.17 7.52 119.00 119.09 10.38 107.00 132.00 25.00
## pub_Baseline 26 5107 1.97 0.80 2.00 1.94 1.48 1.00 5.00 4.00
## pubt_Baseline 27 5096 -0.11 0.77 0.04 -0.12 0.98 -1.61 3.04 4.64
## age_Y1.2 28 5975 131.34 7.75 131.00 131.27 10.38 117.00 149.00 32.00
## pub_Y1 29 2900 2.17 0.88 2.00 2.15 1.48 1.00 5.00 4.00
## pubt_Y1 30 2888 -0.10 0.78 -0.01 -0.10 0.89 -2.12 3.08 5.21
## age_Y2.2 31 5831 144.48 8.03 144.00 144.39 10.38 127.00 168.00 41.00
## pub_Y2 32 5628 2.56 0.95 3.00 2.57 1.48 1.00 5.00 4.00
## pubt_Y2 33 5627 -0.13 0.75 -0.11 -0.11 0.74 -2.84 2.85 5.69
## age_Y3.2 34 5487 155.16 7.76 155.00 155.07 8.90 137.00 175.00 38.00
## pub_Y3 35 5410 3.03 0.93 3.00 3.11 1.48 1.00 5.00 4.00
## pubt_Y3 36 5409 -0.09 0.74 0.03 -0.06 0.69 -2.94 2.15 5.09
## int_p_ave 37 5479 7.76 5.96 6.25 6.96 5.19 0.00 48.50 48.50
## famc_ave 38 6172 2.36 1.62 2.00 2.22 1.48 0.00 8.25 8.25
## bw_lbs 39 5731 7.08 1.49 7.25 7.14 1.39 2.19 13.00 10.81
## matage 40 6127 30.85 5.61 31.00 30.88 5.93 14.00 52.00 38.00
## matalc_ave 41 6055 0.05 0.82 0.00 0.00 0.00 0.00 50.00 50.00
## matcig_ave 42 6060 0.35 2.12 0.00 0.00 0.00 0.00 30.00 30.00
## matmar_ave 43 6063 0.02 0.21 0.00 0.00 0.00 0.00 7.00 7.00
## ses_lt 44 4712 0.29 0.75 0.49 0.40 0.52 -3.69 1.63 5.32
## skew kurtosis se
## age_Baseline 0.05 -1.26 0.10
## int_Baseline 2.54 8.65 0.02
## intt_Baseline 2.52 8.60 0.02
## age_Y1 0.05 -1.20 0.10
## int_Y1 2.46 7.57 0.02
## intt_Y1 2.45 7.52 0.02
## age_Y2 0.10 -0.95 0.11
## int_Y2 2.36 7.00 0.03
## intt_Y2 2.34 6.95 0.03
## age_Y3 0.08 -1.04 0.10
## int_Y3 2.12 5.37 0.03
## intt_Y3 2.10 5.26 0.03
## age_Baseline.1 0.05 -1.26 0.10
## wchr_Baseline 1.10 2.02 0.00
## wchrt_Baseline 1.11 2.03 0.00
## age_Y1.1 0.05 -1.20 0.10
## wchr_Y1 2.80 26.15 0.00
## wchrt_Y1 2.80 26.08 0.00
## age_Y2.1 0.10 -0.95 0.11
## wchr_Y2 1.52 8.17 0.00
## wchrt_Y2 1.52 8.05 0.00
## age_Y3.1 0.08 -1.04 0.10
## wchr_Y3 2.13 13.22 0.00
## wchrt_Y3 2.12 13.19 0.00
## age_Baseline.2 0.05 -1.26 0.10
## pub_Baseline 0.24 -0.89 0.01
## pubt_Baseline 0.15 -0.73 0.01
## age_Y1.2 0.05 -1.20 0.10
## pub_Y1 0.13 -0.86 0.02
## pubt_Y1 0.00 -0.38 0.01
## age_Y2.2 0.10 -0.95 0.11
## pub_Y2 -0.10 -0.76 0.01
## pubt_Y2 -0.27 0.05 0.01
## age_Y3.2 0.08 -1.04 0.10
## pub_Y3 -0.44 -0.40 0.01
## pubt_Y3 -0.38 0.29 0.01
## int_p_ave 1.34 2.14 0.08
## famc_ave 0.71 0.05 0.02
## bw_lbs -0.35 0.05 0.02
## matage -0.02 -0.22 0.07
## matalc_ave 44.32 2429.18 0.01
## matcig_ave 7.55 63.80 0.03
## matmar_ave 17.56 388.64 0.00
## ses_lt -1.69 3.74 0.01
# Select the variables
all_variables <- c("int_Baseline", "int_Y1", "int_Y2", "int_Y3","int_Baseline", "wchr_Baseline", "wchr_Y1", "wchr_Y2", "wchr_Y3","pubt_Baseline", "pub_Y1", "pub_Y2", "pub_Y3")
# Select the variables for age regressed
allt_variables <- c("intt_Baseline", "intt_Y1", "intt_Y2", "intt_Y3","intt_Baseline", "wchrt_Baseline", "wchrt_Y1", "wchrt_Y2", "wchrt_Y3","pubt_Baseline", "pubt_Y1", "pubt_Y2", "pubt_Y3")
# Subset the data for the selected variables
all_data <- data_frame %>%
select(all_of(all_variables))
# Subset the data for the selected t variables
allt_data <- data_frame %>%
select(all_of(allt_variables))
# Compute the correlation matrix for raw vars
cor_matrix <- cor(all_data, use = "pairwise.complete.obs")
cor_matrix
## int_Baseline int_Y1 int_Y2 int_Y3 wchr_Baseline
## int_Baseline 1.000000000 6.358046e-01 0.55293095 0.47270800 0.11290766
## int_Y1 0.635804561 1.000000e+00 0.62038926 0.56217117 0.12904854
## int_Y2 0.552930949 6.203893e-01 1.00000000 0.64081678 0.11388056
## int_Y3 0.472708000 5.621712e-01 0.64081678 1.00000000 0.11973982
## wchr_Baseline 0.112907662 1.290485e-01 0.11388056 0.11973982 1.00000000
## wchr_Y1 0.088159682 1.041213e-01 0.09291038 0.10307698 0.64768274
## wchr_Y2 0.086590027 1.118501e-01 0.11865753 0.12414427 0.65429254
## wchr_Y3 0.066778899 5.306411e-02 0.10879629 0.12367546 0.60950412
## pubt_Baseline 0.037518749 2.880796e-02 0.04517299 0.06839935 0.09999110
## pub_Y1 -0.013795136 -1.519157e-03 0.06457494 0.08706338 0.10466730
## pub_Y2 -0.009858777 1.992103e-05 0.05092155 0.06922206 0.08847407
## pub_Y3 -0.026797664 -7.357648e-03 0.04091553 0.08512584 0.09503871
## wchr_Y1 wchr_Y2 wchr_Y3 pubt_Baseline pub_Y1
## int_Baseline 0.08815968 0.08659003 0.06677890 0.03751875 -0.013795136
## int_Y1 0.10412135 0.11185006 0.05306411 0.02880796 -0.001519157
## int_Y2 0.09291038 0.11865753 0.10879629 0.04517299 0.064574941
## int_Y3 0.10307698 0.12414427 0.12367546 0.06839935 0.087063377
## wchr_Baseline 0.64768274 0.65429254 0.60950412 0.09999110 0.104667303
## wchr_Y1 1.00000000 0.66436617 0.66503848 0.09621152 0.083977453
## wchr_Y2 0.66436617 1.00000000 0.76424120 0.10361335 0.105749095
## wchr_Y3 0.66503848 0.76424120 1.00000000 0.08066410 0.192016907
## pubt_Baseline 0.09621152 0.10361335 0.08066410 1.00000000 0.300922119
## pub_Y1 0.08397745 0.10574909 0.19201691 0.30092212 1.000000000
## pub_Y2 0.05041293 0.05746137 0.11988357 0.16941885 0.620470593
## pub_Y3 0.05077874 0.04481120 0.09563058 0.10934600 0.527886201
## pub_Y2 pub_Y3
## int_Baseline -9.858777e-03 -0.026797664
## int_Y1 1.992103e-05 -0.007357648
## int_Y2 5.092155e-02 0.040915533
## int_Y3 6.922206e-02 0.085125843
## wchr_Baseline 8.847407e-02 0.095038711
## wchr_Y1 5.041293e-02 0.050778744
## wchr_Y2 5.746137e-02 0.044811204
## wchr_Y3 1.198836e-01 0.095630583
## pubt_Baseline 1.694188e-01 0.109345999
## pub_Y1 6.204706e-01 0.527886201
## pub_Y2 1.000000e+00 0.707960442
## pub_Y3 7.079604e-01 1.000000000
# Compute the correlation matrix for age regressed vars
cor_matrix_t <- cor(allt_data, use = "pairwise.complete.obs")
cor_matrix_t
## intt_Baseline intt_Y1 intt_Y2 intt_Y3 wchrt_Baseline
## intt_Baseline 1.00000000 0.63459951 0.55335885 0.47612108 0.11274863
## intt_Y1 0.63459951 1.00000000 0.61992020 0.56421070 0.12888069
## intt_Y2 0.55335885 0.61992020 1.00000000 0.64181976 0.11523869
## intt_Y3 0.47612108 0.56421070 0.64181976 1.00000000 0.12005757
## wchrt_Baseline 0.11274863 0.12888069 0.11523869 0.12005757 1.00000000
## wchrt_Y1 0.08730488 0.10451856 0.09697969 0.10571372 0.64768020
## wchrt_Y2 0.08485624 0.11161811 0.11984600 0.12694736 0.65400281
## wchrt_Y3 0.06787232 0.05496479 0.11039562 0.12093410 0.61119443
## pubt_Baseline 0.03528350 0.02797397 0.04656040 0.07171897 0.09973312
## pubt_Y1 0.03103921 0.02840624 0.06444200 0.08064207 0.12975503
## pubt_Y2 0.02126167 0.01126923 0.04240770 0.05764760 0.12894969
## pubt_Y3 0.00782280 0.01017106 0.04139724 0.07370080 0.13314634
## wchrt_Y1 wchrt_Y2 wchrt_Y3 pubt_Baseline pubt_Y1
## intt_Baseline 0.08730488 0.08485624 0.06787232 0.03528350 0.03103921
## intt_Y1 0.10451856 0.11161811 0.05496479 0.02797397 0.02840624
## intt_Y2 0.09697969 0.11984600 0.11039562 0.04656040 0.06444200
## intt_Y3 0.10571372 0.12694736 0.12093410 0.07171897 0.08064207
## wchrt_Baseline 0.64768020 0.65400281 0.61119443 0.09973312 0.12975503
## wchrt_Y1 1.00000000 0.66408794 0.66740853 0.09586293 0.12909149
## wchrt_Y2 0.66408794 1.00000000 0.76658701 0.10217770 0.14120160
## wchrt_Y3 0.66740853 0.76658701 1.00000000 0.07874789 0.20228954
## pubt_Baseline 0.09586293 0.10217770 0.07874789 1.00000000 0.36851872
## pubt_Y1 0.12909149 0.14120160 0.20228954 0.36851872 1.00000000
## pubt_Y2 0.10501431 0.11444949 0.14590497 0.25344056 0.48920961
## pubt_Y3 0.10611658 0.10424138 0.11103103 0.18049007 0.37453324
## pubt_Y2 pubt_Y3
## intt_Baseline 0.02126167 0.00782280
## intt_Y1 0.01126923 0.01017106
## intt_Y2 0.04240770 0.04139724
## intt_Y3 0.05764760 0.07370080
## wchrt_Baseline 0.12894969 0.13314634
## wchrt_Y1 0.10501431 0.10611658
## wchrt_Y2 0.11444949 0.10424138
## wchrt_Y3 0.14590497 0.11103103
## pubt_Baseline 0.25344056 0.18049007
## pubt_Y1 0.48920961 0.37453324
## pubt_Y2 1.00000000 0.53597078
## pubt_Y3 0.53597078 1.00000000
# Create a ggheatmap
ggheatmap <- ggplot(melt(cor_matrix), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 3) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# Create a ggheatmap for age regressed variables
ggheatmap <- ggplot(melt(cor_matrix_t), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 3) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# Correlation matrix with covariates
# Select the variables for age regressed and covariates
allt_cov_variables <- c("intt_Baseline", "intt_Y1", "intt_Y2", "intt_Y3","intt_Baseline", "wchrt_Baseline", "wchrt_Y1", "wchrt_Y2", "wchrt_Y3","pubt_Baseline", "pubt_Y1", "pubt_Y2", "pubt_Y3", "int_p_ave", "famc_ave", "bw_lbs", "matage", "matalc_ave", "matcig_ave", "matmar_ave", "ses_lt")
# Subset the data for the selected t variables
allt_cov_data <- data_frame %>%
select(all_of(allt_cov_variables))
# Compute the correlation matrix for age regressed vars + covariates
cor_matrix_cov_t <- cor(allt_cov_data, use = "pairwise.complete.obs")
cor_matrix_cov_t
## intt_Baseline intt_Y1 intt_Y2 intt_Y3
## intt_Baseline 1.000000000 0.634599505 0.55335885 0.47612108
## intt_Y1 0.634599505 1.000000000 0.61992020 0.56421070
## intt_Y2 0.553358852 0.619920198 1.00000000 0.64181976
## intt_Y3 0.476121078 0.564210695 0.64181976 1.00000000
## wchrt_Baseline 0.112748632 0.128880689 0.11523869 0.12005757
## wchrt_Y1 0.087304884 0.104518560 0.09697969 0.10571372
## wchrt_Y2 0.084856238 0.111618115 0.11984600 0.12694736
## wchrt_Y3 0.067872323 0.054964793 0.11039562 0.12093410
## pubt_Baseline 0.035283495 0.027973965 0.04656040 0.07171897
## pubt_Y1 0.031039212 0.028406239 0.06444200 0.08064207
## pubt_Y2 0.021261670 0.011269234 0.04240770 0.05764760
## pubt_Y3 0.007822800 0.010171060 0.04139724 0.07370080
## int_p_ave 0.355991007 0.367665693 0.41995073 0.35511429
## famc_ave 0.164928271 0.160871502 0.18473974 0.18633884
## bw_lbs -0.020776935 0.020521616 0.01480217 0.04138705
## matage -0.094918698 -0.067320296 -0.06672996 -0.06156389
## matalc_ave -0.007530688 0.007375519 0.01133506 0.01167891
## matcig_ave 0.054665964 0.076780173 0.06140683 0.07305675
## matmar_ave 0.043304449 0.040361945 0.03272173 0.04603020
## ses_lt -0.255607077 -0.221487877 -0.20859871 -0.21268692
## wchrt_Baseline wchrt_Y1 wchrt_Y2 wchrt_Y3
## intt_Baseline 0.112748632 0.0873048840 0.0848562382 0.0678723232
## intt_Y1 0.128880689 0.1045185595 0.1116181149 0.0549647932
## intt_Y2 0.115238693 0.0969796900 0.1198460047 0.1103956193
## intt_Y3 0.120057569 0.1057137227 0.1269473609 0.1209340993
## wchrt_Baseline 1.000000000 0.6476801956 0.6540028144 0.6111944327
## wchrt_Y1 0.647680196 1.0000000000 0.6640879369 0.6674085270
## wchrt_Y2 0.654002814 0.6640879369 1.0000000000 0.7665870075
## wchrt_Y3 0.611194433 0.6674085270 0.7665870075 1.0000000000
## pubt_Baseline 0.099733120 0.0958629278 0.1021776965 0.0787478853
## pubt_Y1 0.129755027 0.1290914933 0.1412016020 0.2022895353
## pubt_Y2 0.128949686 0.1050143147 0.1144494851 0.1459049692
## pubt_Y3 0.133146340 0.1061165836 0.1042413813 0.1110310300
## int_p_ave 0.074606800 0.0854224387 0.0803676945 0.0284861349
## famc_ave 0.033904905 0.0316720249 0.0328397038 0.0437816231
## bw_lbs 0.063943125 0.0615521215 0.0281735891 0.0476436872
## matage -0.029865891 -0.0390539910 -0.0462294497 -0.0724014572
## matalc_ave 0.009328286 -0.0004684303 0.0007193362 -0.0002777456
## matcig_ave 0.130223896 0.0974661736 0.1154386013 0.2289238300
## matmar_ave 0.038651053 0.0514554115 0.0654811173 0.0628571312
## ses_lt -0.201741488 -0.1588565450 -0.2016745704 -0.2211153951
## pubt_Baseline pubt_Y1 pubt_Y2 pubt_Y3 int_p_ave
## intt_Baseline 0.035283495 0.03103921 0.021261670 0.00782280 0.3559910072
## intt_Y1 0.027973965 0.02840624 0.011269234 0.01017106 0.3676656929
## intt_Y2 0.046560398 0.06444200 0.042407699 0.04139724 0.4199507286
## intt_Y3 0.071718974 0.08064207 0.057647599 0.07370080 0.3551142883
## wchrt_Baseline 0.099733120 0.12975503 0.128949686 0.13314634 0.0746068004
## wchrt_Y1 0.095862928 0.12909149 0.105014315 0.10611658 0.0854224387
## wchrt_Y2 0.102177696 0.14120160 0.114449485 0.10424138 0.0803676945
## wchrt_Y3 0.078747885 0.20228954 0.145904969 0.11103103 0.0284861349
## pubt_Baseline 1.000000000 0.36851872 0.253440563 0.18049007 0.0428625302
## pubt_Y1 0.368518723 1.00000000 0.489209605 0.37453324 0.0473883207
## pubt_Y2 0.253440563 0.48920961 1.000000000 0.53597078 0.0545221315
## pubt_Y3 0.180490066 0.37453324 0.535970775 1.00000000 0.0504080170
## int_p_ave 0.042862530 0.04738832 0.054522131 0.05040802 1.0000000000
## famc_ave 0.008702177 -0.01415702 0.001359803 -0.01843012 0.3114959034
## bw_lbs -0.002006455 0.01304283 -0.024902989 0.01744853 0.0264148549
## matage -0.053661373 -0.01301635 -0.019928182 -0.02183066 -0.1146062137
## matalc_ave 0.003033662 0.01960039 0.024762062 0.00640373 -0.0006641259
## matcig_ave 0.053820414 0.06709481 0.053232745 0.03238020 0.0897095246
## matmar_ave 0.028616833 0.02541711 0.016666477 0.01300408 0.0648147298
## ses_lt -0.126051065 -0.10831892 -0.111125027 -0.10364964 -0.3661287560
## famc_ave bw_lbs matage matalc_ave
## intt_Baseline 0.1649282710 -0.020776935 -0.0949186977 -0.0075306884
## intt_Y1 0.1608715017 0.020521616 -0.0673202957 0.0073755192
## intt_Y2 0.1847397352 0.014802170 -0.0667299564 0.0113350632
## intt_Y3 0.1863388362 0.041387053 -0.0615638851 0.0116789090
## wchrt_Baseline 0.0339049048 0.063943125 -0.0298658914 0.0093282856
## wchrt_Y1 0.0316720249 0.061552122 -0.0390539910 -0.0004684303
## wchrt_Y2 0.0328397038 0.028173589 -0.0462294497 0.0007193362
## wchrt_Y3 0.0437816231 0.047643687 -0.0724014572 -0.0002777456
## pubt_Baseline 0.0087021774 -0.002006455 -0.0536613733 0.0030336621
## pubt_Y1 -0.0141570200 0.013042831 -0.0130163526 0.0196003926
## pubt_Y2 0.0013598035 -0.024902989 -0.0199281822 0.0247620618
## pubt_Y3 -0.0184301173 0.017448529 -0.0218306597 0.0064037301
## int_p_ave 0.3114959034 0.026414855 -0.1146062137 -0.0006641259
## famc_ave 1.0000000000 0.008024666 -0.0005858859 0.0365278092
## bw_lbs 0.0080246657 1.000000000 -0.0499162070 -0.0155825153
## matage -0.0005858859 -0.049916207 1.0000000000 0.0356733281
## matalc_ave 0.0365278092 -0.015582515 0.0356733281 1.0000000000
## matcig_ave 0.0161007397 -0.053714657 -0.1027317078 0.1998483845
## matmar_ave 0.0032220425 -0.005330046 -0.0911399455 0.0320614474
## ses_lt -0.1300038144 0.033712893 0.4577169140 -0.0286423129
## matcig_ave matmar_ave ses_lt
## intt_Baseline 0.05466596 0.043304449 -0.25560708
## intt_Y1 0.07678017 0.040361945 -0.22148788
## intt_Y2 0.06140683 0.032721732 -0.20859871
## intt_Y3 0.07305675 0.046030201 -0.21268692
## wchrt_Baseline 0.13022390 0.038651053 -0.20174149
## wchrt_Y1 0.09746617 0.051455411 -0.15885655
## wchrt_Y2 0.11543860 0.065481117 -0.20167457
## wchrt_Y3 0.22892383 0.062857131 -0.22111540
## pubt_Baseline 0.05382041 0.028616833 -0.12605107
## pubt_Y1 0.06709481 0.025417113 -0.10831892
## pubt_Y2 0.05323275 0.016666477 -0.11112503
## pubt_Y3 0.03238020 0.013004081 -0.10364964
## int_p_ave 0.08970952 0.064814730 -0.36612876
## famc_ave 0.01610074 0.003222043 -0.13000381
## bw_lbs -0.05371466 -0.005330046 0.03371289
## matage -0.10273171 -0.091139946 0.45771691
## matalc_ave 0.19984838 0.032061447 -0.02864231
## matcig_ave 1.00000000 0.106555482 -0.28802399
## matmar_ave 0.10655548 1.000000000 -0.19520129
## ses_lt -0.28802399 -0.195201287 1.00000000
# Create a ggheatmap for age regressed variables + covariates
ggheatmap <- ggplot(melt(cor_matrix_cov_t), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 2) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# subset data to just White participants
subset_all_merged <- all_merged[all_merged$race3 == 2, ]
# Examining cross phenotype associations
# Select the variables to describe
variables_to_describe <- c("age_Baseline", "int_Baseline", "intt_Baseline",
"age_Y1", "int_Y1", "intt_Y1",
"age_Y2", "int_Y2", "intt_Y2",
"age_Y3", "int_Y3", "intt_Y3",
"age_Baseline", "wchr_Baseline", "wchrt_Baseline",
"age_Y1", "wchr_Y1", "wchrt_Y1",
"age_Y2", "wchr_Y2", "wchrt_Y2",
"age_Y3", "wchr_Y3", "wchrt_Y3",
"age_Baseline", "pub_Baseline", "pubt_Baseline",
"age_Y1", "pub_Y1", "pubt_Y1",
"age_Y2", "pub_Y2", "pubt_Y2",
"age_Y3", "pub_Y3", "pubt_Y3",
"int_p_ave", "famc_ave", "bw_lbs",
"matage", "matalc_ave", "matcig_ave",
"matmar_ave", "ses_lt")
# Convert data.table to data frame
data_frame <- as.data.frame(subset_all_merged)
# Use the describe() function to obtain descriptive statistics
variable_descriptions <- describe(data_frame[variables_to_describe])
# Print the descriptive statistics
print(variable_descriptions)
## vars n mean sd median trimmed mad min max range
## age_Baseline 1 1782 118.89 7.28 119.00 118.80 8.90 107.00 132.00 25.00
## int_Baseline 2 1782 1.08 1.82 0.00 0.66 0.00 0.00 12.00 12.00
## intt_Baseline 3 1781 0.06 1.81 -0.91 -0.35 0.34 -1.16 10.86 12.03
## age_Y1 4 1594 130.96 7.51 130.50 130.84 9.64 117.00 149.00 32.00
## int_Y1 5 1593 1.12 1.81 0.00 0.71 0.00 0.00 14.00 14.00
## intt_Y1 6 1592 0.01 1.81 -0.99 -0.38 0.34 -1.30 12.78 14.08
## age_Y2 7 1548 144.22 7.85 144.00 144.13 8.90 127.00 166.00 39.00
## int_Y2 8 1548 1.14 1.90 0.00 0.71 0.00 0.00 15.00 15.00
## intt_Y2 9 1547 -0.09 1.90 -1.04 -0.50 0.55 -1.64 13.66 15.30
## age_Y3 10 1327 154.78 7.65 154.00 154.67 8.90 137.00 173.00 36.00
## int_Y3 11 1327 1.26 2.07 0.00 0.78 0.00 0.00 14.00 14.00
## intt_Y3 12 1326 -0.19 2.07 -1.26 -0.64 0.59 -1.78 12.47 14.25
## age_Baseline.1 13 1782 118.89 7.28 119.00 118.80 8.90 107.00 132.00 25.00
## wchr_Baseline 14 1781 0.49 0.08 0.48 0.49 0.07 0.27 0.89 0.62
## wchrt_Baseline 15 1780 0.01 0.08 0.00 0.00 0.07 -0.21 0.41 0.62
## age_Y1.1 16 1594 130.96 7.51 130.50 130.84 9.64 117.00 149.00 32.00
## wchr_Y1 17 1567 0.50 0.08 0.48 0.49 0.08 0.30 1.00 0.70
## wchrt_Y1 18 1566 0.01 0.08 0.00 0.01 0.08 -0.18 0.52 0.70
## age_Y2.1 19 1548 144.22 7.85 144.00 144.13 8.90 127.00 166.00 39.00
## wchr_Y2 20 1192 0.50 0.10 0.48 0.49 0.08 0.30 1.41 1.10
## wchrt_Y2 21 1192 0.02 0.10 0.00 0.01 0.08 -0.18 0.92 1.10
## age_Y3.1 22 1327 154.78 7.65 154.00 154.67 8.90 137.00 173.00 36.00
## wchr_Y3 23 264 0.52 0.10 0.50 0.51 0.11 0.37 0.94 0.57
## wchrt_Y3 24 264 0.03 0.10 0.01 0.02 0.11 -0.12 0.46 0.58
## age_Baseline.2 25 1782 118.89 7.28 119.00 118.80 8.90 107.00 132.00 25.00
## pub_Baseline 26 1422 2.40 0.85 3.00 2.43 1.48 1.00 5.00 4.00
## pubt_Baseline 27 1410 0.32 0.81 0.44 0.34 0.80 -1.58 3.08 4.66
## age_Y1.2 28 1594 130.96 7.51 130.50 130.84 9.64 117.00 149.00 32.00
## pub_Y1 29 494 2.63 0.87 3.00 2.65 1.48 1.00 5.00 4.00
## pubt_Y1 30 491 0.36 0.75 0.27 0.40 0.77 -2.00 2.47 4.48
## age_Y2.2 31 1548 144.22 7.85 144.00 144.13 8.90 127.00 166.00 39.00
## pub_Y2 32 1421 2.99 0.93 3.00 3.04 1.48 1.00 5.00 4.00
## pubt_Y2 33 1420 0.28 0.75 0.37 0.31 0.71 -2.23 2.82 5.05
## age_Y3.2 34 1327 154.78 7.65 154.00 154.67 8.90 137.00 173.00 36.00
## pub_Y3 35 1239 3.32 0.88 3.00 3.38 1.48 1.00 5.00 4.00
## pubt_Y3 36 1238 0.18 0.70 0.23 0.20 0.52 -2.86 2.31 5.16
## int_p_ave 37 865 6.79 6.40 5.00 5.72 4.45 0.00 46.00 46.00
## famc_ave 38 1784 2.19 1.52 2.00 2.06 1.48 0.00 8.75 8.75
## bw_lbs 39 1554 6.65 1.44 6.69 6.68 1.30 1.81 11.56 9.75
## matage 40 1686 25.79 6.25 25.00 25.38 5.93 13.00 47.00 34.00
## matalc_ave 41 1681 0.01 0.29 0.00 0.00 0.00 0.00 10.00 10.00
## matcig_ave 42 1668 0.44 2.13 0.00 0.00 0.00 0.00 30.00 30.00
## matmar_ave 43 1667 0.07 0.41 0.00 0.00 0.00 0.00 5.00 5.00
## ses_lt 44 990 -0.92 1.02 -0.78 -0.88 1.03 -3.94 1.47 5.41
## skew kurtosis se
## age_Baseline 0.06 -1.25 0.17
## int_Baseline 2.51 7.37 0.04
## intt_Baseline 2.50 7.33 0.04
## age_Y1 0.11 -1.11 0.19
## int_Y1 2.54 8.37 0.05
## intt_Y1 2.54 8.40 0.05
## age_Y2 0.11 -0.87 0.20
## int_Y2 2.69 9.87 0.05
## intt_Y2 2.66 9.67 0.05
## age_Y3 0.12 -0.97 0.21
## int_Y3 2.30 6.05 0.06
## intt_Y3 2.28 5.99 0.06
## age_Baseline.1 0.06 -1.25 0.17
## wchr_Baseline 0.82 0.65 0.00
## wchrt_Baseline 0.82 0.64 0.00
## age_Y1.1 0.11 -1.11 0.19
## wchr_Y1 0.98 1.40 0.00
## wchrt_Y1 0.98 1.40 0.00
## age_Y2.1 0.11 -0.87 0.20
## wchr_Y2 1.79 8.68 0.00
## wchrt_Y2 1.78 8.57 0.00
## age_Y3.1 0.12 -0.97 0.21
## wchr_Y3 0.85 0.55 0.01
## wchrt_Y3 0.85 0.62 0.01
## age_Baseline.2 0.06 -1.25 0.17
## pub_Baseline -0.14 -0.38 0.02
## pubt_Baseline -0.11 -0.15 0.02
## age_Y1.2 0.11 -1.11 0.19
## pub_Y1 -0.22 -0.36 0.04
## pubt_Y1 -0.30 0.20 0.03
## age_Y2.2 0.11 -0.87 0.20
## pub_Y2 -0.30 -0.39 0.02
## pubt_Y2 -0.37 0.53 0.02
## age_Y3.2 0.12 -0.97 0.21
## pub_Y3 -0.59 0.03 0.02
## pubt_Y3 -0.53 1.23 0.02
## int_p_ave 1.86 4.63 0.22
## famc_ave 0.86 0.69 0.04
## bw_lbs -0.21 0.34 0.04
## matage 0.56 -0.11 0.15
## matalc_ave 27.40 865.95 0.01
## matcig_ave 7.71 80.18 0.05
## matmar_ave 7.04 53.82 0.01
## ses_lt -0.44 -0.33 0.03
# Select the variables
all_variables <- c("int_Baseline", "int_Y1", "int_Y2", "int_Y3","int_Baseline", "wchr_Baseline", "wchr_Y1", "wchr_Y2", "wchr_Y3","pubt_Baseline", "pub_Y1", "pub_Y2", "pub_Y3")
# Select the variables for age regressed
allt_variables <- c("intt_Baseline", "intt_Y1", "intt_Y2", "intt_Y3","intt_Baseline", "wchrt_Baseline", "wchrt_Y1", "wchrt_Y2", "wchrt_Y3","pubt_Baseline", "pubt_Y1", "pubt_Y2", "pubt_Y3")
# Subset the data for the selected variables
all_data <- data_frame %>%
select(all_of(all_variables))
# Subset the data for the selected t variables
allt_data <- data_frame %>%
select(all_of(allt_variables))
# Compute the correlation matrix for raw vars
cor_matrix <- cor(all_data, use = "pairwise.complete.obs")
cor_matrix
## int_Baseline int_Y1 int_Y2 int_Y3 wchr_Baseline
## int_Baseline 1.000000000 0.655863739 0.503440509 0.43397481 0.013110800
## int_Y1 0.655863739 1.000000000 0.609926562 0.53776520 0.015426809
## int_Y2 0.503440509 0.609926562 1.000000000 0.64902598 0.028254670
## int_Y3 0.433974812 0.537765203 0.649025984 1.00000000 0.060397820
## wchr_Baseline 0.013110800 0.015426809 0.028254670 0.06039782 1.000000000
## wchr_Y1 -0.005333566 0.017547470 0.030860816 0.06067700 0.818499248
## wchr_Y2 0.049797842 0.035695403 0.038386737 0.08946825 0.713672222
## wchr_Y3 0.186277715 0.159940247 0.081509570 0.18309904 0.743110230
## pubt_Baseline 0.018667504 0.053114251 0.061219612 0.03620911 0.067140044
## pub_Y1 -0.097590642 -0.074158097 -0.046585689 -0.02874867 0.009230123
## pub_Y2 -0.041844352 0.012311033 0.052370593 0.06294933 0.074481288
## pub_Y3 -0.029061549 0.009724576 0.002674181 0.06035082 0.072657964
## wchr_Y1 wchr_Y2 wchr_Y3 pubt_Baseline pub_Y1
## int_Baseline -0.005333566 0.04979784 0.18627772 0.01866750 -0.097590642
## int_Y1 0.017547470 0.03569540 0.15994025 0.05311425 -0.074158097
## int_Y2 0.030860816 0.03838674 0.08150957 0.06121961 -0.046585689
## int_Y3 0.060677005 0.08946825 0.18309904 0.03620911 -0.028748672
## wchr_Baseline 0.818499248 0.71367222 0.74311023 0.06714004 0.009230123
## wchr_Y1 1.000000000 0.77198961 0.79520992 0.10901911 0.017997773
## wchr_Y2 0.771989605 1.00000000 0.82481894 0.05233811 0.019995578
## wchr_Y3 0.795209922 0.82481894 1.00000000 0.10471430 -0.177094340
## pubt_Baseline 0.109019108 0.05233811 0.10471430 1.00000000 0.309587648
## pub_Y1 0.017997773 0.01999558 -0.17709434 0.30958765 1.000000000
## pub_Y2 0.051477173 0.02474927 0.04116280 0.27683917 0.554054984
## pub_Y3 0.059457581 0.07238219 0.06339584 0.21770111 0.475769365
## pub_Y2 pub_Y3
## int_Baseline -0.04184435 -0.029061549
## int_Y1 0.01231103 0.009724576
## int_Y2 0.05237059 0.002674181
## int_Y3 0.06294933 0.060350820
## wchr_Baseline 0.07448129 0.072657964
## wchr_Y1 0.05147717 0.059457581
## wchr_Y2 0.02474927 0.072382192
## wchr_Y3 0.04116280 0.063395843
## pubt_Baseline 0.27683917 0.217701110
## pub_Y1 0.55405498 0.475769365
## pub_Y2 1.00000000 0.598944983
## pub_Y3 0.59894498 1.000000000
# Compute the correlation matrix for age regressed vars
cor_matrix_t <- cor(allt_data, use = "pairwise.complete.obs")
cor_matrix_t
## intt_Baseline intt_Y1 intt_Y2 intt_Y3
## intt_Baseline 1.000000000 0.65637574 0.507173976 0.44155003
## intt_Y1 0.656375737 1.00000000 0.610539764 0.53960614
## intt_Y2 0.507173976 0.61053976 1.000000000 0.64943743
## intt_Y3 0.441550031 0.53960614 0.649437425 1.00000000
## wchrt_Baseline 0.014943580 0.01611478 0.028326488 0.05817768
## wchrt_Y1 -0.003466749 0.01848038 0.031207540 0.05727149
## wchrt_Y2 0.050822395 0.03694035 0.036249387 0.08616715
## wchrt_Y3 0.185391809 0.15763537 0.077449060 0.17741343
## pubt_Baseline 0.022992134 0.05301337 0.057476352 0.02998917
## pubt_Y1 -0.061386057 -0.06488599 -0.019331240 -0.03641045
## pubt_Y2 -0.004360979 0.01659856 0.035883196 0.03093158
## pubt_Y3 0.024865641 0.01752589 -0.006385013 0.02921149
## wchrt_Baseline wchrt_Y1 wchrt_Y2 wchrt_Y3
## intt_Baseline 0.01494358 -0.003466749 0.050822395 0.18539181
## intt_Y1 0.01611478 0.018480376 0.036940347 0.15763537
## intt_Y2 0.02832649 0.031207540 0.036249387 0.07744906
## intt_Y3 0.05817768 0.057271486 0.086167146 0.17741343
## wchrt_Baseline 1.00000000 0.818791678 0.713023475 0.74704380
## wchrt_Y1 0.81879168 1.000000000 0.771999830 0.79889520
## wchrt_Y2 0.71302347 0.771999830 1.000000000 0.82481121
## wchrt_Y3 0.74704380 0.798895203 0.824811207 1.00000000
## pubt_Baseline 0.06835125 0.110798184 0.055570267 0.10815662
## pubt_Y1 0.03593826 0.029604641 0.026677779 -0.13120597
## pubt_Y2 0.07530620 0.043223259 -0.003992287 0.03376282
## pubt_Y3 0.06137307 0.035523913 0.048612998 0.07093859
## pubt_Baseline pubt_Y1 pubt_Y2 pubt_Y3
## intt_Baseline 0.02299213 -0.06138606 -0.004360979 0.024865641
## intt_Y1 0.05301337 -0.06488599 0.016598562 0.017525889
## intt_Y2 0.05747635 -0.01933124 0.035883196 -0.006385013
## intt_Y3 0.02998917 -0.03641045 0.030931585 0.029211487
## wchrt_Baseline 0.06835125 0.03593826 0.075306196 0.061373072
## wchrt_Y1 0.11079818 0.02960464 0.043223259 0.035523913
## wchrt_Y2 0.05557027 0.02667778 -0.003992287 0.048612998
## wchrt_Y3 0.10815662 -0.13120597 0.033762823 0.070938586
## pubt_Baseline 1.00000000 0.29517518 0.264198237 0.164796337
## pubt_Y1 0.29517518 1.00000000 0.390362509 0.243354015
## pubt_Y2 0.26419824 0.39036251 1.000000000 0.383221767
## pubt_Y3 0.16479634 0.24335401 0.383221767 1.000000000
# Create a ggheatmap
ggheatmap <- ggplot(melt(cor_matrix), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 3) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# Create a ggheatmap for age regressed variables
ggheatmap <- ggplot(melt(cor_matrix_t), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 3) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# Correlation matrix with covariates
# Select the variables for age regressed and covariates
allt_cov_variables <- c("intt_Baseline", "intt_Y1", "intt_Y2", "intt_Y3","intt_Baseline", "wchrt_Baseline", "wchrt_Y1", "wchrt_Y2", "wchrt_Y3","pubt_Baseline", "pubt_Y1", "pubt_Y2", "pubt_Y3", "int_p_ave", "famc_ave", "bw_lbs", "matage", "matalc_ave", "matcig_ave", "matmar_ave", "ses_lt")
# Subset the data for the selected t variables
allt_cov_data <- data_frame %>%
select(all_of(allt_cov_variables))
# Compute the correlation matrix for age regressed vars + covariates
cor_matrix_cov_t <- cor(allt_cov_data, use = "pairwise.complete.obs")
cor_matrix_cov_t
## intt_Baseline intt_Y1 intt_Y2 intt_Y3
## intt_Baseline 1.000000000 0.65637574 0.507173976 0.44155003
## intt_Y1 0.656375737 1.00000000 0.610539764 0.53960614
## intt_Y2 0.507173976 0.61053976 1.000000000 0.64943743
## intt_Y3 0.441550031 0.53960614 0.649437425 1.00000000
## wchrt_Baseline 0.014943580 0.01611478 0.028326488 0.05817768
## wchrt_Y1 -0.003466749 0.01848038 0.031207540 0.05727149
## wchrt_Y2 0.050822395 0.03694035 0.036249387 0.08616715
## wchrt_Y3 0.185391809 0.15763537 0.077449060 0.17741343
## pubt_Baseline 0.022992134 0.05301337 0.057476352 0.02998917
## pubt_Y1 -0.061386057 -0.06488599 -0.019331240 -0.03641045
## pubt_Y2 -0.004360979 0.01659856 0.035883196 0.03093158
## pubt_Y3 0.024865641 0.01752589 -0.006385013 0.02921149
## int_p_ave 0.421212163 0.46334330 0.496096102 0.40640343
## famc_ave 0.199746396 0.19949347 0.206924710 0.24817011
## bw_lbs 0.050932027 0.04564774 0.036785459 0.02395145
## matage -0.120387616 -0.06379329 -0.016063842 -0.01453504
## matalc_ave 0.102789525 0.08082069 0.115964165 0.10512536
## matcig_ave 0.113029407 0.14072590 0.132904346 0.14565307
## matmar_ave 0.091859538 0.05433262 0.094259714 0.07870112
## ses_lt -0.323955930 -0.25173088 -0.189327772 -0.16325233
## wchrt_Baseline wchrt_Y1 wchrt_Y2 wchrt_Y3
## intt_Baseline 0.014943580 -0.003466749 0.050822395 0.18539181
## intt_Y1 0.016114779 0.018480376 0.036940347 0.15763537
## intt_Y2 0.028326488 0.031207540 0.036249387 0.07744906
## intt_Y3 0.058177676 0.057271486 0.086167146 0.17741343
## wchrt_Baseline 1.000000000 0.818791678 0.713023475 0.74704380
## wchrt_Y1 0.818791678 1.000000000 0.771999830 0.79889520
## wchrt_Y2 0.713023475 0.771999830 1.000000000 0.82481121
## wchrt_Y3 0.747043804 0.798895203 0.824811207 1.00000000
## pubt_Baseline 0.068351250 0.110798184 0.055570267 0.10815662
## pubt_Y1 0.035938261 0.029604641 0.026677779 -0.13120597
## pubt_Y2 0.075306196 0.043223259 -0.003992287 0.03376282
## pubt_Y3 0.061373072 0.035523913 0.048612998 0.07093859
## int_p_ave 0.017334901 0.046000082 0.053345119 0.21580625
## famc_ave -0.017268735 0.008277899 0.007208506 0.04336610
## bw_lbs 0.130220471 0.110846534 0.141264039 0.17979175
## matage 0.016653165 0.021906118 0.017768144 -0.04200944
## matalc_ave 0.007459777 0.001619303 -0.011034741 -0.10758168
## matcig_ave 0.012351970 0.029485148 0.023419434 0.12011194
## matmar_ave 0.067598406 0.071049717 0.053582267 -0.03054400
## ses_lt -0.112859480 -0.122390813 -0.160839852 -0.06626089
## pubt_Baseline pubt_Y1 pubt_Y2 pubt_Y3
## intt_Baseline 0.022992134 -0.0613860574 -0.0043609786 0.024865641
## intt_Y1 0.053013371 -0.0648859938 0.0165985618 0.017525889
## intt_Y2 0.057476352 -0.0193312404 0.0358831958 -0.006385013
## intt_Y3 0.029989165 -0.0364104515 0.0309315850 0.029211487
## wchrt_Baseline 0.068351250 0.0359382613 0.0753061962 0.061373072
## wchrt_Y1 0.110798184 0.0296046409 0.0432232586 0.035523913
## wchrt_Y2 0.055570267 0.0266777791 -0.0039922865 0.048612998
## wchrt_Y3 0.108156618 -0.1312059705 0.0337628234 0.070938586
## pubt_Baseline 1.000000000 0.2951751783 0.2641982370 0.164796337
## pubt_Y1 0.295175178 1.0000000000 0.3903625085 0.243354015
## pubt_Y2 0.264198237 0.3903625085 1.0000000000 0.383221767
## pubt_Y3 0.164796337 0.2433540149 0.3832217665 1.000000000
## int_p_ave 0.013085427 -0.1087880663 0.0552432734 0.022846031
## famc_ave 0.012626390 -0.0422500879 -0.0007068531 -0.038171373
## bw_lbs -0.007809493 -0.0215455443 0.0693306304 0.001464377
## matage -0.019306092 0.0003169423 -0.0374793519 -0.049681442
## matalc_ave -0.010421519 -0.1053055937 0.0131094417 -0.017661627
## matcig_ave -0.009911451 -0.0316639305 0.0004561523 -0.029974819
## matmar_ave 0.012316210 0.0207164098 0.0099918536 -0.007285697
## ses_lt -0.046380952 0.0512215865 -0.0119463108 -0.039889144
## int_p_ave famc_ave bw_lbs matage
## intt_Baseline 0.42121216 0.1997463958 0.0509320265 -0.1203876156
## intt_Y1 0.46334330 0.1994934739 0.0456477444 -0.0637932854
## intt_Y2 0.49609610 0.2069247105 0.0367854586 -0.0160638420
## intt_Y3 0.40640343 0.2481701085 0.0239514507 -0.0145350419
## wchrt_Baseline 0.01733490 -0.0172687346 0.1302204708 0.0166531646
## wchrt_Y1 0.04600008 0.0082778990 0.1108465338 0.0219061176
## wchrt_Y2 0.05334512 0.0072085059 0.1412640394 0.0177681439
## wchrt_Y3 0.21580625 0.0433661017 0.1797917493 -0.0420094371
## pubt_Baseline 0.01308543 0.0126263899 -0.0078094929 -0.0193060916
## pubt_Y1 -0.10878807 -0.0422500879 -0.0215455443 0.0003169423
## pubt_Y2 0.05524327 -0.0007068531 0.0693306304 -0.0374793519
## pubt_Y3 0.02284603 -0.0381713734 0.0014643773 -0.0496814424
## int_p_ave 1.00000000 0.3607690458 0.0370662595 -0.0140296947
## famc_ave 0.36076905 1.0000000000 -0.0075530403 -0.0809355918
## bw_lbs 0.03706626 -0.0075530403 1.0000000000 0.0147111410
## matage -0.01402969 -0.0809355918 0.0147111410 1.0000000000
## matalc_ave 0.04509212 0.0780947848 -0.0025326558 0.0073298354
## matcig_ave 0.12420204 0.0511935851 -0.0550941285 0.0032175312
## matmar_ave 0.15029203 0.0274925481 -0.0001781025 -0.0394385635
## ses_lt -0.32319718 -0.2797967225 0.0107788491 0.3304060521
## matalc_ave matcig_ave matmar_ave ses_lt
## intt_Baseline 0.102789525 0.1130294065 0.0918595383 -0.32395593
## intt_Y1 0.080820690 0.1407258972 0.0543326243 -0.25173088
## intt_Y2 0.115964165 0.1329043457 0.0942597137 -0.18932777
## intt_Y3 0.105125359 0.1456530689 0.0787011242 -0.16325233
## wchrt_Baseline 0.007459777 0.0123519696 0.0675984059 -0.11285948
## wchrt_Y1 0.001619303 0.0294851477 0.0710497170 -0.12239081
## wchrt_Y2 -0.011034741 0.0234194336 0.0535822670 -0.16083985
## wchrt_Y3 -0.107581678 0.1201119413 -0.0305439977 -0.06626089
## pubt_Baseline -0.010421519 -0.0099114513 0.0123162097 -0.04638095
## pubt_Y1 -0.105305594 -0.0316639305 0.0207164098 0.05122159
## pubt_Y2 0.013109442 0.0004561523 0.0099918536 -0.01194631
## pubt_Y3 -0.017661627 -0.0299748194 -0.0072856969 -0.03988914
## int_p_ave 0.045092120 0.1242020404 0.1502920286 -0.32319718
## famc_ave 0.078094785 0.0511935851 0.0274925481 -0.27979672
## bw_lbs -0.002532656 -0.0550941285 -0.0001781025 0.01077885
## matage 0.007329835 0.0032175312 -0.0394385635 0.33040605
## matalc_ave 1.000000000 0.0553204295 0.0652208584 -0.03140042
## matcig_ave 0.055320430 1.0000000000 0.1954797911 -0.16341217
## matmar_ave 0.065220858 0.1954797911 1.0000000000 -0.10344565
## ses_lt -0.031400424 -0.1634121681 -0.1034456492 1.00000000
# Create a ggheatmap for age regressed variables + covariates
ggheatmap <- ggplot(melt(cor_matrix_cov_t), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 2) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# subset data to just White participants
subset_all_merged <- all_merged[all_merged$race3 == 3, ]
# Examining cross phenotype associations
# Select the variables to describe
variables_to_describe <- c("age_Baseline", "int_Baseline", "intt_Baseline",
"age_Y1", "int_Y1", "intt_Y1",
"age_Y2", "int_Y2", "intt_Y2",
"age_Y3", "int_Y3", "intt_Y3",
"age_Baseline", "wchr_Baseline", "wchrt_Baseline",
"age_Y1", "wchr_Y1", "wchrt_Y1",
"age_Y2", "wchr_Y2", "wchrt_Y2",
"age_Y3", "wchr_Y3", "wchrt_Y3",
"age_Baseline", "pub_Baseline", "pubt_Baseline",
"age_Y1", "pub_Y1", "pubt_Y1",
"age_Y2", "pub_Y2", "pubt_Y2",
"age_Y3", "pub_Y3", "pubt_Y3",
"int_p_ave", "famc_ave", "bw_lbs",
"matage", "matalc_ave", "matcig_ave",
"matmar_ave", "ses_lt")
# Convert data.table to data frame
data_frame <- as.data.frame(subset_all_merged)
# Use the describe() function to obtain descriptive statistics
variable_descriptions <- describe(data_frame[variables_to_describe])
# Print the descriptive statistics
print(variable_descriptions)
## vars n mean sd median trimmed mad min max range
## age_Baseline 1 2408 118.54 7.55 118.00 118.37 10.38 107.00 133.00 26.00
## int_Baseline 2 2408 1.15 1.83 0.00 0.73 0.00 0.00 15.00 15.00
## intt_Baseline 3 2407 0.12 1.83 -0.91 -0.28 0.36 -1.16 13.91 15.08
## age_Y1 4 2215 130.48 7.74 130.00 130.34 10.38 117.00 147.00 30.00
## int_Y1 5 2215 1.22 1.88 0.00 0.80 0.00 0.00 13.00 13.00
## intt_Y1 6 2214 0.12 1.88 -0.97 -0.29 0.40 -1.28 11.91 13.19
## age_Y2 7 2144 143.76 8.07 144.00 143.63 10.38 128.00 165.00 37.00
## int_Y2 8 2144 1.36 2.12 0.00 0.88 0.00 0.00 16.00 16.00
## intt_Y2 9 2143 0.14 2.12 -0.99 -0.32 0.71 -1.62 15.03 16.65
## age_Y3 10 2011 154.40 7.84 154.00 154.27 10.38 138.00 173.00 35.00
## int_Y3 11 2010 1.53 2.22 1.00 1.05 1.48 0.00 15.00 15.00
## intt_Y3 12 2009 0.09 2.21 -0.56 -0.36 1.33 -1.78 13.57 15.36
## age_Baseline.1 13 2408 118.54 7.55 118.00 118.37 10.38 107.00 133.00 26.00
## wchr_Baseline 14 2403 0.51 0.08 0.50 0.50 0.07 0.24 1.37 1.13
## wchrt_Baseline 15 2402 0.03 0.08 0.02 0.02 0.07 -0.24 0.89 1.13
## age_Y1.1 16 2215 130.48 7.74 130.00 130.34 10.38 117.00 147.00 30.00
## wchr_Y1 17 2205 0.51 0.08 0.49 0.50 0.08 0.29 1.47 1.19
## wchrt_Y1 18 2204 0.03 0.08 0.01 0.02 0.08 -0.20 1.00 1.19
## age_Y2.1 19 2144 143.76 8.07 144.00 143.63 10.38 128.00 165.00 37.00
## wchr_Y2 20 1709 0.51 0.08 0.49 0.50 0.08 0.34 0.89 0.55
## wchrt_Y2 21 1708 0.02 0.08 0.01 0.02 0.08 -0.15 0.41 0.56
## age_Y3.1 22 2011 154.40 7.84 154.00 154.27 10.38 138.00 173.00 35.00
## wchr_Y3 23 377 0.52 0.10 0.50 0.51 0.09 0.36 1.19 0.83
## wchrt_Y3 24 376 0.03 0.10 0.01 0.02 0.09 -0.13 0.71 0.83
## age_Baseline.2 25 2408 118.54 7.55 118.00 118.37 10.38 107.00 133.00 26.00
## pub_Baseline 26 1868 2.12 0.86 2.00 2.11 1.48 1.00 5.00 4.00
## pubt_Baseline 27 1857 0.05 0.83 0.05 0.05 1.18 -1.58 3.08 4.66
## age_Y1.2 28 2215 130.48 7.74 130.00 130.34 10.38 117.00 147.00 30.00
## pub_Y1 29 853 2.38 0.92 2.00 2.34 1.48 1.00 5.00 4.00
## pubt_Y1 30 845 0.12 0.82 0.11 0.14 1.07 -2.00 2.83 4.83
## age_Y2.2 31 2144 143.76 8.07 144.00 143.63 10.38 128.00 165.00 37.00
## pub_Y2 32 2018 2.83 0.97 3.00 2.91 1.48 1.00 5.00 4.00
## pubt_Y2 33 2017 0.16 0.79 0.23 0.20 0.75 -2.69 3.02 5.70
## age_Y3.2 34 2011 154.40 7.84 154.00 154.27 10.38 138.00 173.00 35.00
## pub_Y3 35 1946 3.24 0.91 3.00 3.34 1.48 1.00 5.00 4.00
## pubt_Y3 36 1945 0.13 0.74 0.22 0.17 0.57 -2.80 2.53 5.34
## int_p_ave 37 1709 7.73 6.41 6.00 6.78 5.19 0.00 44.00 44.00
## famc_ave 38 2410 2.04 1.44 1.75 1.88 1.48 0.00 7.75 7.75
## bw_lbs 39 2041 7.12 1.46 7.25 7.18 1.20 2.31 14.75 12.44
## matage 40 2348 28.05 6.53 28.00 27.89 7.41 14.00 60.00 46.00
## matalc_ave 41 2358 0.09 1.70 0.00 0.00 0.00 0.00 60.00 60.00
## matcig_ave 42 2357 0.16 1.22 0.00 0.00 0.00 0.00 20.00 20.00
## matmar_ave 43 2355 0.02 0.24 0.00 0.00 0.00 0.00 8.00 8.00
## ses_lt 44 1516 -0.28 0.87 -0.16 -0.20 0.82 -3.55 1.69 5.24
## skew kurtosis se
## age_Baseline 0.13 -1.29 0.15
## int_Baseline 2.41 7.27 0.04
## intt_Baseline 2.41 7.32 0.04
## age_Y1 0.11 -1.20 0.16
## int_Y1 2.24 5.82 0.04
## intt_Y1 2.24 5.84 0.04
## age_Y2 0.14 -0.93 0.17
## int_Y2 2.21 5.76 0.05
## intt_Y2 2.20 5.75 0.05
## age_Y3 0.12 -1.02 0.17
## int_Y3 2.11 5.38 0.05
## intt_Y3 2.09 5.32 0.05
## age_Baseline.1 0.13 -1.29 0.15
## wchr_Baseline 1.54 9.15 0.00
## wchrt_Baseline 1.53 9.12 0.00
## age_Y1.1 0.11 -1.20 0.16
## wchr_Y1 1.67 10.83 0.00
## wchrt_Y1 1.69 10.99 0.00
## age_Y2.1 0.14 -0.93 0.17
## wchr_Y2 0.82 0.73 0.00
## wchrt_Y2 0.82 0.72 0.00
## age_Y3.1 0.12 -1.02 0.17
## wchr_Y3 1.49 6.08 0.00
## wchrt_Y3 1.49 6.17 0.00
## age_Baseline.2 0.13 -1.29 0.15
## pub_Baseline 0.19 -0.67 0.02
## pubt_Baseline 0.14 -0.54 0.02
## age_Y1.2 0.11 -1.20 0.16
## pub_Y1 0.09 -0.61 0.03
## pubt_Y1 -0.10 -0.47 0.03
## age_Y2.2 0.14 -0.93 0.17
## pub_Y2 -0.33 -0.74 0.02
## pubt_Y2 -0.41 0.27 0.02
## age_Y3.2 0.12 -1.02 0.17
## pub_Y3 -0.74 0.06 0.02
## pubt_Y3 -0.67 1.28 0.02
## int_p_ave 1.51 2.82 0.16
## famc_ave 0.97 0.77 0.03
## bw_lbs -0.26 1.27 0.03
## matage 0.24 -0.44 0.13
## matalc_ave 30.13 977.83 0.03
## matcig_ave 10.15 122.82 0.03
## matmar_ave 22.05 617.96 0.00
## ses_lt -0.85 0.78 0.02
# Select the variables
all_variables <- c("int_Baseline", "int_Y1", "int_Y2", "int_Y3","int_Baseline", "wchr_Baseline", "wchr_Y1", "wchr_Y2", "wchr_Y3","pubt_Baseline", "pub_Y1", "pub_Y2", "pub_Y3")
# Select the variables for age regressed
allt_variables <- c("intt_Baseline", "intt_Y1", "intt_Y2", "intt_Y3","intt_Baseline", "wchrt_Baseline", "wchrt_Y1", "wchrt_Y2", "wchrt_Y3","pubt_Baseline", "pubt_Y1", "pubt_Y2", "pubt_Y3")
# Subset the data for the selected variables
all_data <- data_frame %>%
select(all_of(all_variables))
# Subset the data for the selected t variables
allt_data <- data_frame %>%
select(all_of(allt_variables))
# Compute the correlation matrix for raw vars
cor_matrix <- cor(all_data, use = "pairwise.complete.obs")
cor_matrix
## int_Baseline int_Y1 int_Y2 int_Y3 wchr_Baseline
## int_Baseline 1.000000000 0.60270184 0.53573295 0.45969906 0.09454427
## int_Y1 0.602701839 1.00000000 0.61055859 0.52220674 0.12586763
## int_Y2 0.535732952 0.61055859 1.00000000 0.59985887 0.06617289
## int_Y3 0.459699062 0.52220674 0.59985887 1.00000000 0.05164465
## wchr_Baseline 0.094544274 0.12586763 0.06617289 0.05164465 1.00000000
## wchr_Y1 0.099934435 0.11913154 0.06413020 0.05049886 0.69846646
## wchr_Y2 0.115930357 0.12025362 0.09455314 0.06966075 0.70542276
## wchr_Y3 0.143438323 0.13914024 0.12771713 0.08898117 0.60822109
## pubt_Baseline 0.060521398 0.04969004 0.04601167 0.04895839 0.05675837
## pub_Y1 0.037607918 0.03431242 0.05961206 0.12920160 0.01090057
## pub_Y2 -0.004755301 0.03114834 0.06488880 0.10469344 0.04700926
## pub_Y3 -0.015533902 0.00916158 0.04618651 0.09871629 0.02207483
## wchr_Y1 wchr_Y2 wchr_Y3 pubt_Baseline pub_Y1
## int_Baseline 0.09993444 0.115930357 0.143438323 0.06052140 0.037607918
## int_Y1 0.11913154 0.120253621 0.139140242 0.04969004 0.034312415
## int_Y2 0.06413020 0.094553143 0.127717127 0.04601167 0.059612061
## int_Y3 0.05049886 0.069660749 0.088981175 0.04895839 0.129201597
## wchr_Baseline 0.69846646 0.705422762 0.608221092 0.05675837 0.010900572
## wchr_Y1 1.00000000 0.723965704 0.668435879 0.08157854 0.026174999
## wchr_Y2 0.72396570 1.000000000 0.759213903 0.03268638 0.016207111
## wchr_Y3 0.66843588 0.759213903 1.000000000 0.10556143 0.005042967
## pubt_Baseline 0.08157854 0.032686383 0.105561431 1.00000000 0.353156151
## pub_Y1 0.02617500 0.016207111 0.005042967 0.35315615 1.000000000
## pub_Y2 0.03183742 0.007751155 0.023242962 0.16249673 0.598526450
## pub_Y3 0.01669336 -0.014237700 0.036714716 0.11999840 0.476614501
## pub_Y2 pub_Y3
## int_Baseline -0.004755301 -0.01553390
## int_Y1 0.031148338 0.00916158
## int_Y2 0.064888799 0.04618651
## int_Y3 0.104693437 0.09871629
## wchr_Baseline 0.047009263 0.02207483
## wchr_Y1 0.031837423 0.01669336
## wchr_Y2 0.007751155 -0.01423770
## wchr_Y3 0.023242962 0.03671472
## pubt_Baseline 0.162496725 0.11999840
## pub_Y1 0.598526450 0.47661450
## pub_Y2 1.000000000 0.66285692
## pub_Y3 0.662856922 1.00000000
# Compute the correlation matrix for age regressed vars
cor_matrix_t <- cor(allt_data, use = "pairwise.complete.obs")
cor_matrix_t
## intt_Baseline intt_Y1 intt_Y2 intt_Y3 wchrt_Baseline
## intt_Baseline 1.00000000 0.603720391 0.53768290 0.46415259 0.092323723
## intt_Y1 0.60372039 1.000000000 0.61140292 0.52273514 0.125363372
## intt_Y2 0.53768290 0.611402921 1.00000000 0.60102226 0.065148034
## intt_Y3 0.46415259 0.522735145 0.60102226 1.00000000 0.053290041
## wchrt_Baseline 0.09232372 0.125363372 0.06514803 0.05329004 1.000000000
## wchrt_Y1 0.09805673 0.117990878 0.06252368 0.05176862 0.698481618
## wchrt_Y2 0.11318929 0.120366887 0.09493121 0.07125195 0.705873964
## wchrt_Y3 0.13985359 0.135677900 0.12610888 0.08721631 0.607958244
## pubt_Baseline 0.06139324 0.050491447 0.04687600 0.04931916 0.056569346
## pubt_Y1 0.04623119 0.035995541 0.06446826 0.10873906 0.005945939
## pubt_Y2 0.01779463 0.026820446 0.05920282 0.07923883 0.082531990
## pubt_Y3 0.02000534 0.007965359 0.04697729 0.07382540 0.050518150
## wchrt_Y1 wchrt_Y2 wchrt_Y3 pubt_Baseline pubt_Y1
## intt_Baseline 0.09805673 0.113189289 0.139853593 0.06139324 0.046231189
## intt_Y1 0.11799088 0.120366887 0.135677900 0.05049145 0.035995541
## intt_Y2 0.06252368 0.094931211 0.126108882 0.04687600 0.064468265
## intt_Y3 0.05176862 0.071251952 0.087216312 0.04931916 0.108739064
## wchrt_Baseline 0.69848162 0.705873964 0.607958244 0.05656935 0.005945939
## wchrt_Y1 1.00000000 0.723671336 0.668069856 0.08182391 0.029249838
## wchrt_Y2 0.72367134 1.000000000 0.753390907 0.03225415 0.018635579
## wchrt_Y3 0.66806986 0.753390907 1.000000000 0.10863128 -0.007676958
## pubt_Baseline 0.08182391 0.032254154 0.108631280 1.00000000 0.380396584
## pubt_Y1 0.02924984 0.018635579 -0.007676958 0.38039658 1.000000000
## pubt_Y2 0.06642228 0.029472318 0.047922812 0.18874959 0.475962894
## pubt_Y3 0.04206637 0.004175147 0.069888631 0.13212961 0.298293974
## pubt_Y2 pubt_Y3
## intt_Baseline 0.01779463 0.020005343
## intt_Y1 0.02682045 0.007965359
## intt_Y2 0.05920282 0.046977286
## intt_Y3 0.07923883 0.073825398
## wchrt_Baseline 0.08253199 0.050518150
## wchrt_Y1 0.06642228 0.042066366
## wchrt_Y2 0.02947232 0.004175147
## wchrt_Y3 0.04792281 0.069888631
## pubt_Baseline 0.18874959 0.132129608
## pubt_Y1 0.47596289 0.298293974
## pubt_Y2 1.00000000 0.486616517
## pubt_Y3 0.48661652 1.000000000
# Create a ggheatmap
ggheatmap <- ggplot(melt(cor_matrix), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 3) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# Create a ggheatmap for age regressed variables
ggheatmap <- ggplot(melt(cor_matrix_t), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 3) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# Correlation matrix with covariates
# Select the variables for age regressed and covariates
allt_cov_variables <- c("intt_Baseline", "intt_Y1", "intt_Y2", "intt_Y3","intt_Baseline", "wchrt_Baseline", "wchrt_Y1", "wchrt_Y2", "wchrt_Y3","pubt_Baseline", "pubt_Y1", "pubt_Y2", "pubt_Y3", "int_p_ave", "famc_ave", "bw_lbs", "matage", "matalc_ave", "matcig_ave", "matmar_ave", "ses_lt")
# Subset the data for the selected t variables
allt_cov_data <- data_frame %>%
select(all_of(allt_cov_variables))
# Compute the correlation matrix for age regressed vars + covariates
cor_matrix_cov_t <- cor(allt_cov_data, use = "pairwise.complete.obs")
cor_matrix_cov_t
## intt_Baseline intt_Y1 intt_Y2 intt_Y3
## intt_Baseline 1.00000000 0.603720391 0.53768290 0.464152588
## intt_Y1 0.60372039 1.000000000 0.61140292 0.522735145
## intt_Y2 0.53768290 0.611402921 1.00000000 0.601022258
## intt_Y3 0.46415259 0.522735145 0.60102226 1.000000000
## wchrt_Baseline 0.09232372 0.125363372 0.06514803 0.053290041
## wchrt_Y1 0.09805673 0.117990878 0.06252368 0.051768620
## wchrt_Y2 0.11318929 0.120366887 0.09493121 0.071251952
## wchrt_Y3 0.13985359 0.135677900 0.12610888 0.087216312
## pubt_Baseline 0.06139324 0.050491447 0.04687600 0.049319159
## pubt_Y1 0.04623119 0.035995541 0.06446826 0.108739064
## pubt_Y2 0.01779463 0.026820446 0.05920282 0.079238827
## pubt_Y3 0.02000534 0.007965359 0.04697729 0.073825398
## int_p_ave 0.39802507 0.358965237 0.41077528 0.371097061
## famc_ave 0.15641981 0.163385528 0.19324598 0.209520102
## bw_lbs 0.03091150 0.003882456 0.01241335 0.034920092
## matage -0.05820601 -0.042519441 -0.01300179 -0.005191954
## matalc_ave 0.05829631 -0.005235779 0.04812132 0.041237859
## matcig_ave 0.05176057 0.041966486 0.05597373 0.016352509
## matmar_ave 0.01046488 0.026685492 0.06230283 0.050609554
## ses_lt -0.23172843 -0.219789909 -0.19164979 -0.127749362
## wchrt_Baseline wchrt_Y1 wchrt_Y2 wchrt_Y3
## intt_Baseline 0.092323723 0.098056734 0.113189289 0.139853593
## intt_Y1 0.125363372 0.117990878 0.120366887 0.135677900
## intt_Y2 0.065148034 0.062523675 0.094931211 0.126108882
## intt_Y3 0.053290041 0.051768620 0.071251952 0.087216312
## wchrt_Baseline 1.000000000 0.698481618 0.705873964 0.607958244
## wchrt_Y1 0.698481618 1.000000000 0.723671336 0.668069856
## wchrt_Y2 0.705873964 0.723671336 1.000000000 0.753390907
## wchrt_Y3 0.607958244 0.668069856 0.753390907 1.000000000
## pubt_Baseline 0.056569346 0.081823905 0.032254154 0.108631280
## pubt_Y1 0.005945939 0.029249838 0.018635579 -0.007676958
## pubt_Y2 0.082531990 0.066422277 0.029472318 0.047922812
## pubt_Y3 0.050518150 0.042066366 0.004175147 0.069888631
## int_p_ave -0.001131163 0.021013622 0.010626439 0.049046181
## famc_ave -0.013168505 -0.020786542 -0.004645044 0.003833479
## bw_lbs 0.103886218 0.093191320 0.067325013 0.134118184
## matage -0.019130041 -0.058032497 -0.067553489 -0.111749381
## matalc_ave 0.005262163 0.001560344 0.010001695 0.017350556
## matcig_ave 0.011188076 0.014562535 0.022865979 0.027520079
## matmar_ave -0.016476663 -0.008182653 -0.021216292 -0.023136886
## ses_lt -0.166460886 -0.203522314 -0.228930643 -0.280561970
## pubt_Baseline pubt_Y1 pubt_Y2 pubt_Y3 int_p_ave
## intt_Baseline 0.06139324 0.046231189 0.01779463 0.020005343 0.398025070
## intt_Y1 0.05049145 0.035995541 0.02682045 0.007965359 0.358965237
## intt_Y2 0.04687600 0.064468265 0.05920282 0.046977286 0.410775280
## intt_Y3 0.04931916 0.108739064 0.07923883 0.073825398 0.371097061
## wchrt_Baseline 0.05656935 0.005945939 0.08253199 0.050518150 -0.001131163
## wchrt_Y1 0.08182391 0.029249838 0.06642228 0.042066366 0.021013622
## wchrt_Y2 0.03225415 0.018635579 0.02947232 0.004175147 0.010626439
## wchrt_Y3 0.10863128 -0.007676958 0.04792281 0.069888631 0.049046181
## pubt_Baseline 1.00000000 0.380396584 0.18874959 0.132129608 0.054358132
## pubt_Y1 0.38039658 1.000000000 0.47596289 0.298293974 0.057456511
## pubt_Y2 0.18874959 0.475962894 1.00000000 0.486616517 0.023157794
## pubt_Y3 0.13212961 0.298293974 0.48661652 1.000000000 0.015359679
## int_p_ave 0.05435813 0.057456511 0.02315779 0.015359679 1.000000000
## famc_ave -0.02931147 0.006127340 -0.02205536 -0.023596372 0.345549516
## bw_lbs 0.01664035 0.065298714 -0.02989370 0.005317744 -0.015245219
## matage -0.06517651 -0.032213583 -0.05022099 -0.063829021 0.039997818
## matalc_ave -0.01364370 0.049010256 0.01744267 -0.004821990 0.102346312
## matcig_ave -0.02721274 0.041490279 -0.01906328 -0.021173379 0.117468264
## matmar_ave -0.01021247 0.086462859 0.01336075 -0.012205886 0.024485176
## ses_lt -0.08718362 -0.229177033 -0.09178337 -0.087562200 -0.293825883
## famc_ave bw_lbs matage matalc_ave matcig_ave
## intt_Baseline 0.156419806 0.030911498 -0.058206009 0.058296307 0.05176057
## intt_Y1 0.163385528 0.003882456 -0.042519441 -0.005235779 0.04196649
## intt_Y2 0.193245975 0.012413348 -0.013001792 0.048121324 0.05597373
## intt_Y3 0.209520102 0.034920092 -0.005191954 0.041237859 0.01635251
## wchrt_Baseline -0.013168505 0.103886218 -0.019130041 0.005262163 0.01118808
## wchrt_Y1 -0.020786542 0.093191320 -0.058032497 0.001560344 0.01456253
## wchrt_Y2 -0.004645044 0.067325013 -0.067553489 0.010001695 0.02286598
## wchrt_Y3 0.003833479 0.134118184 -0.111749381 0.017350556 0.02752008
## pubt_Baseline -0.029311469 0.016640351 -0.065176508 -0.013643697 -0.02721274
## pubt_Y1 0.006127340 0.065298714 -0.032213583 0.049010256 0.04149028
## pubt_Y2 -0.022055356 -0.029893698 -0.050220988 0.017442668 -0.01906328
## pubt_Y3 -0.023596372 0.005317744 -0.063829021 -0.004821990 -0.02117338
## int_p_ave 0.345549516 -0.015245219 0.039997818 0.102346312 0.11746826
## famc_ave 1.000000000 0.008368085 0.071715358 0.034574058 0.02902606
## bw_lbs 0.008368085 1.000000000 -0.039544397 -0.016125638 -0.01186736
## matage 0.071715358 -0.039544397 1.000000000 0.025645751 -0.02100861
## matalc_ave 0.034574058 -0.016125638 0.025645751 1.000000000 0.31481809
## matcig_ave 0.029026057 -0.011867359 -0.021008608 0.314818091 1.00000000
## matmar_ave 0.045960005 0.024582786 -0.043360747 0.028942061 0.01389411
## ses_lt -0.105304875 -0.041207534 0.432337811 -0.041312985 -0.15423670
## matmar_ave ses_lt
## intt_Baseline 0.010464876 -0.23172843
## intt_Y1 0.026685492 -0.21978991
## intt_Y2 0.062302830 -0.19164979
## intt_Y3 0.050609554 -0.12774936
## wchrt_Baseline -0.016476663 -0.16646089
## wchrt_Y1 -0.008182653 -0.20352231
## wchrt_Y2 -0.021216292 -0.22893064
## wchrt_Y3 -0.023136886 -0.28056197
## pubt_Baseline -0.010212470 -0.08718362
## pubt_Y1 0.086462859 -0.22917703
## pubt_Y2 0.013360747 -0.09178337
## pubt_Y3 -0.012205886 -0.08756220
## int_p_ave 0.024485176 -0.29382588
## famc_ave 0.045960005 -0.10530488
## bw_lbs 0.024582786 -0.04120753
## matage -0.043360747 0.43233781
## matalc_ave 0.028942061 -0.04131299
## matcig_ave 0.013894111 -0.15423670
## matmar_ave 1.000000000 -0.06786295
## ses_lt -0.067862948 1.00000000
# Create a ggheatmap for age regressed variables + covariates
ggheatmap <- ggplot(melt(cor_matrix_cov_t), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 2) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# subset data to just White participants
subset_all_merged <- all_merged[all_merged$sex == 1, ]
# Examining cross phenotype associations
# Select the variables to describe
variables_to_describe <- c("age_Baseline", "int_Baseline", "intt_Baseline",
"age_Y1", "int_Y1", "intt_Y1",
"age_Y2", "int_Y2", "intt_Y2",
"age_Y3", "int_Y3", "intt_Y3",
"age_Baseline", "wchr_Baseline", "wchrt_Baseline",
"age_Y1", "wchr_Y1", "wchrt_Y1",
"age_Y2", "wchr_Y2", "wchrt_Y2",
"age_Y3", "wchr_Y3", "wchrt_Y3",
"age_Baseline", "pub_Baseline", "pubt_Baseline",
"age_Y1", "pub_Y1", "pubt_Y1",
"age_Y2", "pub_Y2", "pubt_Y2",
"age_Y3", "pub_Y3", "pubt_Y3",
"int_p_ave", "famc_ave", "bw_lbs",
"matage", "matalc_ave", "matcig_ave",
"matmar_ave", "ses_lt")
# Convert data.table to data frame
data_frame <- as.data.frame(subset_all_merged)
# Use the describe() function to obtain descriptive statistics
variable_descriptions <- describe(data_frame[variables_to_describe])
# Print the descriptive statistics
print(variable_descriptions)
## vars n mean sd median trimmed mad min max range
## age_Baseline 1 5415 119.13 7.50 119.00 119.04 10.38 107.00 133.00 26.00
## int_Baseline 2 5415 1.11 1.78 0.00 0.72 0.00 0.00 15.00 15.00
## intt_Baseline 3 5415 0.00 1.78 -1.07 -0.39 0.12 -1.16 13.91 15.08
## age_Y1 4 5130 131.23 7.70 131.00 131.16 10.38 117.00 149.00 32.00
## int_Y1 5 5129 1.15 1.82 0.00 0.74 0.00 0.00 14.00 14.00
## intt_Y1 6 5129 0.00 1.82 -1.05 -0.40 0.29 -1.30 12.78 14.08
## age_Y2 7 5013 144.41 7.96 144.00 144.35 10.38 127.00 166.00 39.00
## int_Y2 8 5013 1.23 1.95 0.00 0.79 0.00 0.00 15.00 15.00
## intt_Y2 9 5013 0.00 1.95 -1.05 -0.42 0.52 -1.55 13.87 15.41
## age_Y3 10 4665 155.12 7.75 155.00 155.03 8.90 137.00 174.00 37.00
## int_Y3 11 4662 1.35 2.00 1.00 0.92 1.48 0.00 15.00 15.00
## intt_Y3 12 4662 0.00 2.00 -0.50 -0.42 1.30 -1.56 13.69 15.25
## age_Baseline.1 13 5415 119.13 7.50 119.00 119.04 10.38 107.00 133.00 26.00
## wchr_Baseline 14 5409 0.48 0.07 0.47 0.47 0.06 0.30 1.15 0.85
## wchrt_Baseline 15 5409 0.00 0.07 -0.01 -0.01 0.06 -0.18 0.66 0.85
## age_Y1.1 16 5130 131.23 7.70 131.00 131.16 10.38 117.00 149.00 32.00
## wchr_Y1 17 5092 0.48 0.07 0.47 0.47 0.06 0.29 1.38 1.10
## wchrt_Y1 18 5092 0.00 0.07 -0.02 -0.01 0.06 -0.20 0.90 1.10
## age_Y2.1 19 5013 144.41 7.96 144.00 144.35 10.38 127.00 166.00 39.00
## wchr_Y2 20 4203 0.48 0.08 0.46 0.47 0.06 0.31 1.41 1.10
## wchrt_Y2 21 4203 0.00 0.08 -0.02 -0.01 0.06 -0.18 0.92 1.10
## age_Y3.1 22 4665 155.12 7.75 155.00 155.03 8.90 137.00 174.00 37.00
## wchr_Y3 23 921 0.49 0.08 0.46 0.48 0.07 0.36 1.19 0.83
## wchrt_Y3 24 921 0.00 0.08 -0.02 -0.01 0.07 -0.13 0.71 0.84
## age_Baseline.2 25 5415 119.13 7.50 119.00 119.04 10.38 107.00 133.00 26.00
## pub_Baseline 26 4797 1.94 0.77 2.00 1.90 1.48 1.00 5.00 4.00
## pubt_Baseline 27 4792 0.00 0.76 0.05 -0.04 1.44 -0.97 3.08 4.05
## age_Y1.2 28 5130 131.23 7.70 131.00 131.16 10.38 117.00 149.00 32.00
## pub_Y1 29 2285 1.95 0.77 2.00 1.91 1.48 1.00 5.00 4.00
## pubt_Y1 30 2280 0.00 0.76 0.02 -0.03 1.22 -1.13 3.08 4.21
## age_Y2.2 31 5013 144.41 7.96 144.00 144.35 10.38 127.00 166.00 39.00
## pub_Y2 32 4915 2.23 0.83 2.00 2.22 1.48 1.00 5.00 4.00
## pubt_Y2 33 4915 0.00 0.79 -0.02 0.00 0.82 -1.86 3.02 4.87
## age_Y3.2 34 4665 155.12 7.75 155.00 155.03 8.90 137.00 174.00 37.00
## pub_Y3 35 4650 2.66 0.86 3.00 2.70 1.48 1.00 5.00 4.00
## pubt_Y3 36 4650 0.00 0.81 0.04 0.02 0.80 -2.15 2.53 4.68
## int_p_ave 37 4257 7.72 6.08 6.25 6.88 5.19 0.00 48.50 48.50
## famc_ave 38 5418 2.34 1.60 2.00 2.19 1.48 0.00 8.25 8.25
## bw_lbs 39 4884 7.15 1.50 7.31 7.21 1.39 2.12 14.75 12.62
## matage 40 5305 29.44 6.26 30.00 29.43 5.93 14.00 60.00 46.00
## matalc_ave 41 5279 0.06 1.17 0.00 0.00 0.00 0.00 60.00 60.00
## matcig_ave 42 5276 0.32 1.98 0.00 0.00 0.00 0.00 30.00 30.00
## matmar_ave 43 5278 0.02 0.24 0.00 0.00 0.00 0.00 8.00 8.00
## ses_lt 44 3752 -0.01 0.93 0.24 0.11 0.76 -3.94 1.59 5.53
## skew kurtosis se
## age_Baseline 0.05 -1.27 0.10
## int_Baseline 2.40 7.58 0.02
## intt_Baseline 2.40 7.58 0.02
## age_Y1 0.06 -1.18 0.11
## int_Y1 2.33 6.54 0.03
## intt_Y1 2.32 6.51 0.03
## age_Y2 0.08 -0.96 0.11
## int_Y2 2.35 6.84 0.03
## intt_Y2 2.33 6.77 0.03
## age_Y3 0.08 -1.02 0.11
## int_Y3 2.04 4.82 0.03
## intt_Y3 2.03 4.77 0.03
## age_Baseline.1 0.05 -1.27 0.10
## wchr_Baseline 1.22 3.10 0.00
## wchrt_Baseline 1.22 3.09 0.00
## age_Y1.1 0.06 -1.18 0.11
## wchr_Y1 1.56 7.33 0.00
## wchrt_Y1 1.55 7.30 0.00
## age_Y2.1 0.08 -0.96 0.11
## wchr_Y2 1.81 10.53 0.00
## wchrt_Y2 1.81 10.48 0.00
## age_Y3.1 0.08 -1.02 0.11
## wchr_Y3 1.65 6.13 0.00
## wchrt_Y3 1.65 6.24 0.00
## age_Baseline.2 0.05 -1.27 0.10
## pub_Baseline 0.42 -0.19 0.01
## pubt_Baseline 0.43 -0.18 0.01
## age_Y1.2 0.06 -1.18 0.11
## pub_Y1 0.34 -0.54 0.02
## pubt_Y1 0.31 -0.57 0.02
## age_Y2.2 0.08 -0.96 0.11
## pub_Y2 0.09 -0.63 0.01
## pubt_Y2 0.07 -0.50 0.01
## age_Y3.2 0.08 -1.02 0.11
## pub_Y3 -0.23 -0.43 0.01
## pubt_Y3 -0.20 -0.27 0.01
## int_p_ave 1.45 2.83 0.09
## famc_ave 0.77 0.15 0.02
## bw_lbs -0.28 0.35 0.02
## matage 0.03 -0.36 0.09
## matalc_ave 42.13 1979.22 0.02
## matcig_ave 8.52 86.75 0.03
## matmar_ave 17.43 379.99 0.00
## ses_lt -1.19 1.25 0.02
# Select the variables
all_variables <- c("int_Baseline", "int_Y1", "int_Y2", "int_Y3","int_Baseline", "wchr_Baseline", "wchr_Y1", "wchr_Y2", "wchr_Y3","pubt_Baseline", "pub_Y1", "pub_Y2", "pub_Y3")
# Select the variables for age regressed
allt_variables <- c("intt_Baseline", "intt_Y1", "intt_Y2", "intt_Y3","intt_Baseline", "wchrt_Baseline", "wchrt_Y1", "wchrt_Y2", "wchrt_Y3","pubt_Baseline", "pubt_Y1", "pubt_Y2", "pubt_Y3")
# Subset the data for the selected variables
all_data <- data_frame %>%
select(all_of(all_variables))
# Subset the data for the selected t variables
allt_data <- data_frame %>%
select(all_of(allt_variables))
# Compute the correlation matrix for raw vars
cor_matrix <- cor(all_data, use = "pairwise.complete.obs")
cor_matrix
## int_Baseline int_Y1 int_Y2 int_Y3 wchr_Baseline
## int_Baseline 1.00000000 0.63137491 0.557009005 0.50274008 0.08433382
## int_Y1 0.63137491 1.00000000 0.631225908 0.58359069 0.11097593
## int_Y2 0.55700900 0.63122591 1.000000000 0.65311341 0.07908891
## int_Y3 0.50274008 0.58359069 0.653113413 1.00000000 0.08329643
## wchr_Baseline 0.08433382 0.11097593 0.079088914 0.08329643 1.00000000
## wchr_Y1 0.08902741 0.11479823 0.095304982 0.09081946 0.74767938
## wchr_Y2 0.09652095 0.11268561 0.090420599 0.09973608 0.69810278
## wchr_Y3 0.12183640 0.10932637 0.066873910 0.08781948 0.70568090
## pubt_Baseline 0.03080922 0.01777874 0.012165741 0.03984321 0.04647517
## pub_Y1 0.01459254 -0.01900346 0.009925341 0.03400911 0.11052342
## pub_Y2 0.02494770 0.03511006 0.030144740 0.02719693 0.12450868
## pub_Y3 0.03246799 0.03344152 0.045155382 0.06054504 0.11950641
## wchr_Y1 wchr_Y2 wchr_Y3 pubt_Baseline pub_Y1
## int_Baseline 0.08902741 0.09652095 0.12183640 0.03080922 0.014592542
## int_Y1 0.11479823 0.11268561 0.10932637 0.01777874 -0.019003458
## int_Y2 0.09530498 0.09042060 0.06687391 0.01216574 0.009925341
## int_Y3 0.09081946 0.09973608 0.08781948 0.03984321 0.034009114
## wchr_Baseline 0.74767938 0.69810278 0.70568090 0.04647517 0.110523420
## wchr_Y1 1.00000000 0.74419085 0.74822625 0.06466979 0.085901713
## wchr_Y2 0.74419085 1.00000000 0.80498405 0.04257451 0.064460817
## wchr_Y3 0.74822625 0.80498405 1.00000000 0.06211216 0.080434964
## pubt_Baseline 0.06466979 0.04257451 0.06211216 1.00000000 0.309839341
## pub_Y1 0.08590171 0.06446082 0.08043496 0.30983934 1.000000000
## pub_Y2 0.08543944 0.04210667 0.04729826 0.20078123 0.442810016
## pub_Y3 0.08306037 0.03883849 0.05209544 0.14792894 0.335288247
## pub_Y2 pub_Y3
## int_Baseline 0.02494770 0.03246799
## int_Y1 0.03511006 0.03344152
## int_Y2 0.03014474 0.04515538
## int_Y3 0.02719693 0.06054504
## wchr_Baseline 0.12450868 0.11950641
## wchr_Y1 0.08543944 0.08306037
## wchr_Y2 0.04210667 0.03883849
## wchr_Y3 0.04729826 0.05209544
## pubt_Baseline 0.20078123 0.14792894
## pub_Y1 0.44281002 0.33528825
## pub_Y2 1.00000000 0.54959889
## pub_Y3 0.54959889 1.00000000
# Compute the correlation matrix for age regressed vars
cor_matrix_t <- cor(allt_data, use = "pairwise.complete.obs")
cor_matrix_t
## intt_Baseline intt_Y1 intt_Y2 intt_Y3 wchrt_Baseline
## intt_Baseline 1.00000000 0.63125420 0.557213452 0.50296261 0.08451420
## intt_Y1 0.63125420 1.00000000 0.630861855 0.58330574 0.11116722
## intt_Y2 0.55721345 0.63086186 1.000000000 0.65273515 0.08009033
## intt_Y3 0.50296261 0.58330574 0.652735152 1.00000000 0.08352809
## wchrt_Baseline 0.08451420 0.11116722 0.080090327 0.08352809 1.00000000
## wchrt_Y1 0.08935389 0.11534966 0.097428562 0.09172473 0.74766747
## wchrt_Y2 0.09645357 0.11496886 0.093230534 0.10107766 0.69754146
## wchrt_Y3 0.11865054 0.10778030 0.066966183 0.08518181 0.70844093
## pubt_Baseline 0.03080895 0.01777450 0.013278505 0.04055903 0.04647451
## pubt_Y1 0.01940711 -0.02065375 0.003826305 0.03079306 0.11260606
## pubt_Y2 0.02042759 0.02392324 0.012080869 0.01746976 0.13457457
## pubt_Y3 0.02922030 0.02198955 0.026405710 0.04690541 0.12991048
## wchrt_Y1 wchrt_Y2 wchrt_Y3 pubt_Baseline pubt_Y1
## intt_Baseline 0.08935389 0.09645357 0.11865054 0.03080895 0.019407106
## intt_Y1 0.11534966 0.11496886 0.10778030 0.01777450 -0.020653749
## intt_Y2 0.09742856 0.09323053 0.06696618 0.01327851 0.003826305
## intt_Y3 0.09172473 0.10107766 0.08518181 0.04055903 0.030793061
## wchrt_Baseline 0.74766747 0.69754146 0.70844093 0.04647451 0.112606062
## wchrt_Y1 1.00000000 0.74315473 0.75024380 0.06467818 0.091493882
## wchrt_Y2 0.74315473 1.00000000 0.80465842 0.04207712 0.072316154
## wchrt_Y3 0.75024380 0.80465842 1.00000000 0.06054914 0.098243650
## pubt_Baseline 0.06467818 0.04207712 0.06054914 1.00000000 0.315295526
## pubt_Y1 0.09149388 0.07231615 0.09824365 0.31529553 1.000000000
## pubt_Y2 0.09930383 0.05847450 0.06974588 0.21833934 0.428090051
## pubt_Y3 0.09607269 0.06256240 0.08146684 0.16057450 0.311742329
## pubt_Y2 pubt_Y3
## intt_Baseline 0.02042759 0.02922030
## intt_Y1 0.02392324 0.02198955
## intt_Y2 0.01208087 0.02640571
## intt_Y3 0.01746976 0.04690541
## wchrt_Baseline 0.13457457 0.12991048
## wchrt_Y1 0.09930383 0.09607269
## wchrt_Y2 0.05847450 0.06256240
## wchrt_Y3 0.06974588 0.08146684
## pubt_Baseline 0.21833934 0.16057450
## pubt_Y1 0.42809005 0.31174233
## pubt_Y2 1.00000000 0.49600684
## pubt_Y3 0.49600684 1.00000000
# Create a ggheatmap
ggheatmap <- ggplot(melt(cor_matrix), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 3) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# Create a ggheatmap for age regressed variables
ggheatmap <- ggplot(melt(cor_matrix_t), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 3) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# Correlation matrix with covariates
# Select the variables for age regressed and covariates
allt_cov_variables <- c("intt_Baseline", "intt_Y1", "intt_Y2", "intt_Y3","intt_Baseline", "wchrt_Baseline", "wchrt_Y1", "wchrt_Y2", "wchrt_Y3","pubt_Baseline", "pubt_Y1", "pubt_Y2", "pubt_Y3", "int_p_ave", "famc_ave", "bw_lbs", "matage", "matalc_ave", "matcig_ave", "matmar_ave", "ses_lt")
# Subset the data for the selected t variables
allt_cov_data <- data_frame %>%
select(all_of(allt_cov_variables))
# Compute the correlation matrix for age regressed vars + covariates
cor_matrix_cov_t <- cor(allt_cov_data, use = "pairwise.complete.obs")
cor_matrix_cov_t
## intt_Baseline intt_Y1 intt_Y2 intt_Y3
## intt_Baseline 1.000000000 0.631254199 0.557213452 0.50296261
## intt_Y1 0.631254199 1.000000000 0.630861855 0.58330574
## intt_Y2 0.557213452 0.630861855 1.000000000 0.65273515
## intt_Y3 0.502962607 0.583305739 0.652735152 1.00000000
## wchrt_Baseline 0.084514198 0.111167220 0.080090327 0.08352809
## wchrt_Y1 0.089353887 0.115349665 0.097428562 0.09172473
## wchrt_Y2 0.096453575 0.114968864 0.093230534 0.10107766
## wchrt_Y3 0.118650538 0.107780301 0.066966183 0.08518181
## pubt_Baseline 0.030808954 0.017774500 0.013278505 0.04055903
## pubt_Y1 0.019407106 -0.020653749 0.003826305 0.03079306
## pubt_Y2 0.020427590 0.023923240 0.012080869 0.01746976
## pubt_Y3 0.029220300 0.021989548 0.026405710 0.04690541
## int_p_ave 0.380730511 0.376291987 0.415035788 0.39165549
## famc_ave 0.157363958 0.151224621 0.177818110 0.19113549
## bw_lbs 0.005894143 0.035554114 0.019942268 0.04774002
## matage -0.091071163 -0.055436917 -0.041212711 -0.04562174
## matalc_ave 0.005876354 0.008004406 0.010572779 0.03333859
## matcig_ave 0.045336190 0.081263670 0.058955535 0.07926922
## matmar_ave 0.029880325 0.029168800 0.045904470 0.06565199
## ses_lt -0.235159691 -0.186202068 -0.146156352 -0.13567424
## wchrt_Baseline wchrt_Y1 wchrt_Y2 wchrt_Y3
## intt_Baseline 0.0845141977 0.089353887 0.096453575 0.118650538
## intt_Y1 0.1111672197 0.115349665 0.114968864 0.107780301
## intt_Y2 0.0800903273 0.097428562 0.093230534 0.066966183
## intt_Y3 0.0835280942 0.091724727 0.101077664 0.085181814
## wchrt_Baseline 1.0000000000 0.747667475 0.697541460 0.708440926
## wchrt_Y1 0.7476674745 1.000000000 0.743154727 0.750243804
## wchrt_Y2 0.6975414596 0.743154727 1.000000000 0.804658420
## wchrt_Y3 0.7084409258 0.750243804 0.804658420 1.000000000
## pubt_Baseline 0.0464745103 0.064678178 0.042077122 0.060549143
## pubt_Y1 0.1126060621 0.091493882 0.072316154 0.098243650
## pubt_Y2 0.1345745729 0.099303831 0.058474497 0.069745883
## pubt_Y3 0.1299104787 0.096072685 0.062562400 0.081466839
## int_p_ave 0.0357380293 0.061920215 0.049731542 0.000578575
## famc_ave -0.0290801406 -0.000213522 0.004451835 0.012420872
## bw_lbs 0.0720766911 0.077824082 0.040242591 0.115462055
## matage -0.0556474551 -0.066376624 -0.052465847 -0.068276519
## matalc_ave -0.0001409895 -0.002992378 -0.001816142 -0.025093432
## matcig_ave 0.0499428738 0.059046443 0.062030450 0.065648688
## matmar_ave 0.0249198685 0.053754828 0.030068912 0.026475711
## ses_lt -0.2098365881 -0.204643243 -0.222508322 -0.181440131
## pubt_Baseline pubt_Y1 pubt_Y2 pubt_Y3
## intt_Baseline 0.0308089543 0.019407106 0.020427590 0.029220300
## intt_Y1 0.0177745000 -0.020653749 0.023923240 0.021989548
## intt_Y2 0.0132785053 0.003826305 0.012080869 0.026405710
## intt_Y3 0.0405590258 0.030793061 0.017469763 0.046905409
## wchrt_Baseline 0.0464745103 0.112606062 0.134574573 0.129910479
## wchrt_Y1 0.0646781778 0.091493882 0.099303831 0.096072685
## wchrt_Y2 0.0420771222 0.072316154 0.058474497 0.062562400
## wchrt_Y3 0.0605491432 0.098243650 0.069745883 0.081466839
## pubt_Baseline 1.0000000000 0.315295526 0.218339340 0.160574500
## pubt_Y1 0.3152955259 1.000000000 0.428090051 0.311742329
## pubt_Y2 0.2183393395 0.428090051 1.000000000 0.496006844
## pubt_Y3 0.1605745000 0.311742329 0.496006844 1.000000000
## int_p_ave 0.0115126271 0.030889228 0.044751373 0.050204735
## famc_ave 0.0049669555 -0.007158929 -0.011140275 -0.019790730
## bw_lbs -0.0248568614 -0.004313385 -0.030521319 0.003415325
## matage -0.0640713015 -0.063410704 -0.086962413 -0.077476546
## matalc_ave -0.0087923468 0.024972342 0.015895671 -0.001272120
## matcig_ave -0.0007969318 0.037376831 0.007156975 -0.003615720
## matmar_ave 0.0234927700 0.048160683 0.010250948 -0.002571770
## ses_lt -0.1334724578 -0.185162639 -0.168097056 -0.150742501
## int_p_ave famc_ave bw_lbs matage matalc_ave
## intt_Baseline 0.380730511 0.157363958 0.005894143 -0.09107116 0.0058763541
## intt_Y1 0.376291987 0.151224621 0.035554114 -0.05543692 0.0080044056
## intt_Y2 0.415035788 0.177818110 0.019942268 -0.04121271 0.0105727793
## intt_Y3 0.391655488 0.191135488 0.047740023 -0.04562174 0.0333385949
## wchrt_Baseline 0.035738029 -0.029080141 0.072076691 -0.05564746 -0.0001409895
## wchrt_Y1 0.061920215 -0.000213522 0.077824082 -0.06637662 -0.0029923778
## wchrt_Y2 0.049731542 0.004451835 0.040242591 -0.05246585 -0.0018161424
## wchrt_Y3 0.000578575 0.012420872 0.115462055 -0.06827652 -0.0250934324
## pubt_Baseline 0.011512627 0.004966955 -0.024856861 -0.06407130 -0.0087923468
## pubt_Y1 0.030889228 -0.007158929 -0.004313385 -0.06341070 0.0249723420
## pubt_Y2 0.044751373 -0.011140275 -0.030521319 -0.08696241 0.0158956707
## pubt_Y3 0.050204735 -0.019790730 0.003415325 -0.07747655 -0.0012721199
## int_p_ave 1.000000000 0.316216791 0.009654146 -0.06480519 0.0311110462
## famc_ave 0.316216791 1.000000000 -0.006105543 0.03024307 0.0273598891
## bw_lbs 0.009654146 -0.006105543 1.000000000 -0.01644323 -0.0252080537
## matage -0.064805191 0.030243072 -0.016443226 1.00000000 0.0235248570
## matalc_ave 0.031111046 0.027359889 -0.025208054 0.02352486 1.0000000000
## matcig_ave 0.090940984 0.012280219 -0.057818237 -0.06272207 0.1720746778
## matmar_ave 0.047274166 0.004164946 0.003002555 -0.06999346 0.0056198206
## ses_lt -0.303517135 -0.112991371 0.069781405 0.48541854 -0.0230118254
## matcig_ave matmar_ave ses_lt
## intt_Baseline 0.0453361897 0.029880325 -0.23515969
## intt_Y1 0.0812636699 0.029168800 -0.18620207
## intt_Y2 0.0589555350 0.045904470 -0.14615635
## intt_Y3 0.0792692219 0.065651987 -0.13567424
## wchrt_Baseline 0.0499428738 0.024919868 -0.20983659
## wchrt_Y1 0.0590464432 0.053754828 -0.20464324
## wchrt_Y2 0.0620304500 0.030068912 -0.22250832
## wchrt_Y3 0.0656486883 0.026475711 -0.18144013
## pubt_Baseline -0.0007969318 0.023492770 -0.13347246
## pubt_Y1 0.0373768311 0.048160683 -0.18516264
## pubt_Y2 0.0071569755 0.010250948 -0.16809706
## pubt_Y3 -0.0036157205 -0.002571770 -0.15074250
## int_p_ave 0.0909409839 0.047274166 -0.30351714
## famc_ave 0.0122802186 0.004164946 -0.11299137
## bw_lbs -0.0578182367 0.003002555 0.06978141
## matage -0.0627220731 -0.069993457 0.48541854
## matalc_ave 0.1720746778 0.005619821 -0.02301183
## matcig_ave 1.0000000000 0.101755827 -0.20072201
## matmar_ave 0.1017558267 1.000000000 -0.14068118
## ses_lt -0.2007220053 -0.140681178 1.00000000
# Create a ggheatmap for age regressed variables + covariates
ggheatmap <- ggplot(melt(cor_matrix_cov_t), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 2) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# subset data to just White participants
subset_all_merged <- all_merged[all_merged$sex == 2, ]
# Examining cross phenotype associations
# Select the variables to describe
variables_to_describe <- c("age_Baseline", "int_Baseline", "intt_Baseline",
"age_Y1", "int_Y1", "intt_Y1",
"age_Y2", "int_Y2", "intt_Y2",
"age_Y3", "int_Y3", "intt_Y3",
"age_Baseline", "wchr_Baseline", "wchrt_Baseline",
"age_Y1", "wchr_Y1", "wchrt_Y1",
"age_Y2", "wchr_Y2", "wchrt_Y2",
"age_Y3", "wchr_Y3", "wchrt_Y3",
"age_Baseline", "pub_Baseline", "pubt_Baseline",
"age_Y1", "pub_Y1", "pubt_Y1",
"age_Y2", "pub_Y2", "pubt_Y2",
"age_Y3", "pub_Y3", "pubt_Y3",
"int_p_ave", "famc_ave", "bw_lbs",
"matage", "matalc_ave", "matcig_ave",
"matmar_ave", "ses_lt")
# Convert data.table to data frame
data_frame <- as.data.frame(subset_all_merged)
# Use the describe() function to obtain descriptive statistics
variable_descriptions <- describe(data_frame[variables_to_describe])
# Print the descriptive statistics
print(variable_descriptions)
## vars n mean sd median trimmed mad min max range
## age_Baseline 1 4942 118.81 7.48 119.00 118.68 10.38 107.00 132.00 25.00
## int_Baseline 2 4940 0.93 1.61 0.00 0.56 0.00 0.00 13.00 13.00
## intt_Baseline 3 4940 0.00 1.61 -0.91 -0.37 0.06 -0.96 12.06 13.02
## age_Y1 4 4651 130.91 7.74 131.00 130.80 10.38 117.00 149.00 32.00
## int_Y1 5 4651 1.06 1.72 0.00 0.68 0.00 0.00 14.00 14.00
## intt_Y1 6 4651 0.00 1.72 -0.98 -0.37 0.24 -1.19 12.91 14.10
## age_Y2 7 4507 144.12 8.07 144.00 143.98 10.38 128.00 168.00 40.00
## int_Y2 8 4506 1.24 1.98 0.00 0.79 0.00 0.00 16.00 16.00
## intt_Y2 9 4506 0.00 1.97 -1.00 -0.42 0.68 -1.70 15.03 16.73
## age_Y3 10 4158 154.71 7.78 154.00 154.59 8.90 137.00 175.00 38.00
## int_Y3 11 4157 1.54 2.30 1.00 1.04 1.48 0.00 15.00 15.00
## intt_Y3 12 4157 0.00 2.30 -0.67 -0.48 1.36 -1.82 13.57 15.40
## age_Baseline.1 13 4942 118.81 7.48 119.00 118.68 10.38 107.00 132.00 25.00
## wchr_Baseline 14 4933 0.48 0.07 0.47 0.47 0.06 0.24 1.37 1.13
## wchrt_Baseline 15 4933 0.00 0.07 -0.01 -0.01 0.06 -0.24 0.89 1.13
## age_Y1.1 16 4651 130.91 7.74 131.00 130.80 10.38 117.00 149.00 32.00
## wchr_Y1 17 4619 0.48 0.08 0.47 0.47 0.06 0.27 1.47 1.21
## wchrt_Y1 18 4619 0.00 0.08 -0.02 -0.01 0.06 -0.21 1.00 1.21
## age_Y2.1 19 4507 144.12 8.07 144.00 143.98 10.38 128.00 168.00 40.00
## wchr_Y2 20 3714 0.48 0.08 0.46 0.47 0.07 0.26 1.02 0.76
## wchrt_Y2 21 3714 0.00 0.08 -0.02 -0.01 0.07 -0.22 0.54 0.76
## age_Y3.1 22 4158 154.71 7.78 154.00 154.59 8.90 137.00 175.00 38.00
## wchr_Y3 23 833 0.49 0.09 0.47 0.48 0.07 0.34 1.26 0.92
## wchrt_Y3 24 833 0.00 0.09 -0.02 -0.01 0.07 -0.15 0.77 0.92
## age_Baseline.2 25 4942 118.81 7.48 119.00 118.68 10.38 107.00 132.00 25.00
## pub_Baseline 26 3598 2.26 0.89 2.00 2.28 1.48 1.00 5.00 4.00
## pubt_Baseline 27 3571 0.00 0.86 0.05 0.02 0.98 -1.61 2.93 4.54
## age_Y1.2 28 4651 130.91 7.74 131.00 130.80 10.38 117.00 149.00 32.00
## pub_Y1 29 1961 2.64 0.89 3.00 2.67 0.00 1.00 5.00 4.00
## pubt_Y1 30 1944 0.00 0.84 0.11 0.03 0.78 -2.12 2.83 4.95
## age_Y2.2 31 4507 144.12 8.07 144.00 143.98 10.38 128.00 168.00 40.00
## pub_Y2 32 4149 3.24 0.82 3.00 3.33 1.48 1.00 5.00 4.00
## pubt_Y2 33 4149 0.00 0.76 0.08 0.06 0.63 -2.84 2.31 5.15
## age_Y3.2 34 4158 154.71 7.78 154.00 154.59 8.90 137.00 175.00 38.00
## pub_Y3 35 3942 3.66 0.69 4.00 3.72 0.00 1.00 5.00 4.00
## pubt_Y3 36 3942 0.00 0.66 0.14 0.05 0.57 -2.94 1.66 4.60
## int_p_ave 37 3793 7.57 6.15 5.75 6.69 4.82 0.00 46.00 46.00
## famc_ave 38 4945 2.16 1.53 2.00 2.02 1.48 0.00 8.75 8.75
## bw_lbs 39 4439 6.86 1.45 7.00 6.92 1.30 1.81 13.00 11.19
## matage 40 4853 29.28 6.25 29.00 29.27 7.41 13.00 52.00 39.00
## matalc_ave 41 4814 0.05 0.89 0.00 0.00 0.00 0.00 50.00 50.00
## matcig_ave 42 4808 0.33 1.92 0.00 0.00 0.00 0.00 25.00 25.00
## matmar_ave 43 4806 0.03 0.28 0.00 0.00 0.00 0.00 7.00 7.00
## ses_lt 44 3465 0.03 0.92 0.27 0.15 0.75 -3.80 1.69 5.49
## skew kurtosis se
## age_Baseline 0.09 -1.26 0.11
## int_Baseline 2.65 8.79 0.02
## intt_Baseline 2.65 8.79 0.02
## age_Y1 0.09 -1.18 0.11
## int_Y1 2.54 8.24 0.03
## intt_Y1 2.54 8.23 0.03
## age_Y2 0.14 -0.90 0.12
## int_Y2 2.41 7.40 0.03
## intt_Y2 2.39 7.37 0.03
## age_Y3 0.12 -1.02 0.12
## int_Y3 2.17 5.52 0.04
## intt_Y3 2.16 5.51 0.04
## age_Baseline.1 0.09 -1.26 0.11
## wchr_Baseline 1.39 6.54 0.00
## wchrt_Baseline 1.39 6.53 0.00
## age_Y1.1 0.09 -1.18 0.11
## wchr_Y1 2.45 18.85 0.00
## wchrt_Y1 2.46 18.85 0.00
## age_Y2.1 0.14 -0.90 0.12
## wchr_Y2 1.18 2.48 0.00
## wchrt_Y2 1.18 2.46 0.00
## age_Y3.1 0.12 -1.02 0.12
## wchr_Y3 1.79 8.32 0.00
## wchrt_Y3 1.79 8.32 0.00
## age_Baseline.2 0.09 -1.26 0.11
## pub_Baseline -0.16 -1.01 0.01
## pubt_Baseline -0.16 -0.97 0.01
## age_Y1.2 0.09 -1.18 0.11
## pub_Y1 -0.39 -0.38 0.02
## pubt_Y1 -0.35 -0.29 0.02
## age_Y2.2 0.14 -0.90 0.12
## pub_Y2 -0.76 0.63 0.01
## pubt_Y2 -0.73 0.94 0.01
## age_Y3.2 0.12 -1.02 0.12
## pub_Y3 -1.11 2.31 0.01
## pubt_Y3 -1.01 2.27 0.01
## int_p_ave 1.43 2.36 0.10
## famc_ave 0.83 0.44 0.02
## bw_lbs -0.37 0.27 0.02
## matage 0.02 -0.52 0.09
## matalc_ave 42.34 2154.34 0.01
## matcig_ave 7.60 65.91 0.03
## matmar_ave 11.51 168.08 0.00
## ses_lt -1.24 1.40 0.02
# Select the variables
all_variables <- c("int_Baseline", "int_Y1", "int_Y2", "int_Y3","int_Baseline", "wchr_Baseline", "wchr_Y1", "wchr_Y2", "wchr_Y3","pubt_Baseline", "pub_Y1", "pub_Y2", "pub_Y3")
# Select the variables for age regressed
allt_variables <- c("intt_Baseline", "intt_Y1", "intt_Y2", "intt_Y3","intt_Baseline", "wchrt_Baseline", "wchrt_Y1", "wchrt_Y2", "wchrt_Y3","pubt_Baseline", "pubt_Y1", "pubt_Y2", "pubt_Y3")
# Subset the data for the selected variables
all_data <- data_frame %>%
select(all_of(all_variables))
# Subset the data for the selected t variables
allt_data <- data_frame %>%
select(all_of(allt_variables))
# Compute the correlation matrix for raw vars
cor_matrix <- cor(all_data, use = "pairwise.complete.obs")
cor_matrix
## int_Baseline int_Y1 int_Y2 int_Y3 wchr_Baseline
## int_Baseline 1.000000000 0.63006350 0.52310491 0.43399629 0.10572124
## int_Y1 0.630063500 1.00000000 0.60147440 0.52138570 0.10913467
## int_Y2 0.523104906 0.60147440 1.00000000 0.61575279 0.09407256
## int_Y3 0.433996286 0.52138570 0.61575279 1.00000000 0.09155619
## wchr_Baseline 0.105721243 0.10913467 0.09407256 0.09155619 1.00000000
## wchr_Y1 0.073842085 0.07946399 0.05955349 0.07207802 0.68767929
## wchr_Y2 0.086120394 0.09235673 0.10309424 0.10145914 0.69955008
## wchr_Y3 0.099190212 0.08318337 0.14616349 0.14505898 0.61345435
## pubt_Baseline 0.067207131 0.07240414 0.08618011 0.07027137 0.18867303
## pub_Y1 0.029367627 0.06751000 0.10111957 0.10496780 0.11479372
## pub_Y2 0.034208870 0.03487927 0.10900282 0.09878727 0.14869608
## pub_Y3 0.004310881 0.01433558 0.07051692 0.09752480 0.12638938
## wchr_Y1 wchr_Y2 wchr_Y3 pubt_Baseline pub_Y1
## int_Baseline 0.07384209 0.08612039 0.09919021 0.06720713 0.02936763
## int_Y1 0.07946399 0.09235673 0.08318337 0.07240414 0.06751000
## int_Y2 0.05955349 0.10309424 0.14616349 0.08618011 0.10111957
## int_Y3 0.07207802 0.10145914 0.14505898 0.07027137 0.10496780
## wchr_Baseline 0.68767929 0.69955008 0.61345435 0.18867303 0.11479372
## wchr_Y1 1.00000000 0.69273414 0.68083349 0.18532971 0.13210842
## wchr_Y2 0.69273414 1.00000000 0.77097119 0.19604803 0.18818961
## wchr_Y3 0.68083349 0.77097119 1.00000000 0.19080757 0.18174839
## pubt_Baseline 0.18532971 0.19604803 0.19080757 1.00000000 0.43338964
## pub_Y1 0.13210842 0.18818961 0.18174839 0.43338964 1.00000000
## pub_Y2 0.12712810 0.18295262 0.23946693 0.31900504 0.62422135
## pub_Y3 0.11684900 0.15264140 0.18617456 0.23393737 0.50676969
## pub_Y2 pub_Y3
## int_Baseline 0.03420887 0.004310881
## int_Y1 0.03487927 0.014335584
## int_Y2 0.10900282 0.070516920
## int_Y3 0.09878727 0.097524801
## wchr_Baseline 0.14869608 0.126389376
## wchr_Y1 0.12712810 0.116848997
## wchr_Y2 0.18295262 0.152641402
## wchr_Y3 0.23946693 0.186174560
## pubt_Baseline 0.31900504 0.233937366
## pub_Y1 0.62422135 0.506769687
## pub_Y2 1.00000000 0.608902237
## pub_Y3 0.60890224 1.000000000
# Compute the correlation matrix for age regressed vars
cor_matrix_t <- cor(allt_data, use = "pairwise.complete.obs")
cor_matrix_t
## intt_Baseline intt_Y1 intt_Y2 intt_Y3 wchrt_Baseline
## intt_Baseline 1.000000000 0.630156041 0.52360544 0.43398938 0.10594075
## intt_Y1 0.630156041 1.000000000 0.60072899 0.52103491 0.10965719
## intt_Y2 0.523605440 0.600728992 1.00000000 0.61551840 0.09526343
## intt_Y3 0.433989378 0.521034910 0.61551840 1.00000000 0.09150144
## wchrt_Baseline 0.105940753 0.109657195 0.09526343 0.09150144 1.00000000
## wchrt_Y1 0.074114430 0.080570086 0.06212365 0.07248302 0.68773094
## wchrt_Y2 0.086126974 0.092143054 0.10225210 0.10149756 0.69955142
## wchrt_Y3 0.099129687 0.082958874 0.14614328 0.14494450 0.61353884
## pubt_Baseline 0.067202313 0.072375380 0.08644283 0.07062126 0.18874495
## pubt_Y1 0.043722404 0.077045354 0.10684269 0.10832306 0.12798975
## pubt_Y2 0.029214014 0.018085413 0.08732790 0.09396024 0.16537334
## pubt_Y3 0.003475505 0.005261589 0.05557784 0.08932391 0.13445550
## wchrt_Y1 wchrt_Y2 wchrt_Y3 pubt_Baseline pubt_Y1
## intt_Baseline 0.07411443 0.08612697 0.09912969 0.06720231 0.04372240
## intt_Y1 0.08057009 0.09214305 0.08295887 0.07237538 0.07704535
## intt_Y2 0.06212365 0.10225210 0.14614328 0.08644283 0.10684269
## intt_Y3 0.07248302 0.10149756 0.14494450 0.07062126 0.10832306
## wchrt_Baseline 0.68773094 0.69955142 0.61353884 0.18874495 0.12798975
## wchrt_Y1 1.00000000 0.69334961 0.68076633 0.18559341 0.15488544
## wchrt_Y2 0.69334961 1.00000000 0.77075601 0.19604711 0.19499036
## wchrt_Y3 0.68076633 0.77075601 1.00000000 0.19078156 0.18733246
## pubt_Baseline 0.18559341 0.19604711 0.19078156 1.00000000 0.46338874
## pubt_Y1 0.15488544 0.19499036 0.18733246 0.46338874 1.00000000
## pubt_Y2 0.15280124 0.18959174 0.24251300 0.34311227 0.57353711
## pubt_Y3 0.12861559 0.15826707 0.18581825 0.24355756 0.44338060
## pubt_Y2 pubt_Y3
## intt_Baseline 0.02921401 0.003475505
## intt_Y1 0.01808541 0.005261589
## intt_Y2 0.08732790 0.055577840
## intt_Y3 0.09396024 0.089323913
## wchrt_Baseline 0.16537334 0.134455498
## wchrt_Y1 0.15280124 0.128615590
## wchrt_Y2 0.18959174 0.158267074
## wchrt_Y3 0.24251300 0.185818250
## pubt_Baseline 0.34311227 0.243557557
## pubt_Y1 0.57353711 0.443380600
## pubt_Y2 1.00000000 0.561569185
## pubt_Y3 0.56156918 1.000000000
# Create a ggheatmap
ggheatmap <- ggplot(melt(cor_matrix), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 3) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# Create a ggheatmap for age regressed variables
ggheatmap <- ggplot(melt(cor_matrix_t), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 3) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# Correlation matrix with covariates
# Select the variables for age regressed and covariates
allt_cov_variables <- c("intt_Baseline", "intt_Y1", "intt_Y2", "intt_Y3","intt_Baseline", "wchrt_Baseline", "wchrt_Y1", "wchrt_Y2", "wchrt_Y3","pubt_Baseline", "pubt_Y1", "pubt_Y2", "pubt_Y3", "int_p_ave", "famc_ave", "bw_lbs", "matage", "matalc_ave", "matcig_ave", "matmar_ave", "ses_lt")
# Subset the data for the selected t variables
allt_cov_data <- data_frame %>%
select(all_of(allt_cov_variables))
# Compute the correlation matrix for age regressed vars + covariates
cor_matrix_cov_t <- cor(allt_cov_data, use = "pairwise.complete.obs")
cor_matrix_cov_t
## intt_Baseline intt_Y1 intt_Y2 intt_Y3
## intt_Baseline 1.000000000 0.630156041 0.52360544 0.43398938
## intt_Y1 0.630156041 1.000000000 0.60072899 0.52103491
## intt_Y2 0.523605440 0.600728992 1.00000000 0.61551840
## intt_Y3 0.433989378 0.521034910 0.61551840 1.00000000
## wchrt_Baseline 0.105940753 0.109657195 0.09526343 0.09150144
## wchrt_Y1 0.074114430 0.080570086 0.06212365 0.07248302
## wchrt_Y2 0.086126974 0.092143054 0.10225210 0.10149756
## wchrt_Y3 0.099129687 0.082958874 0.14614328 0.14494450
## pubt_Baseline 0.067202313 0.072375380 0.08644283 0.07062126
## pubt_Y1 0.043722404 0.077045354 0.10684269 0.10832306
## pubt_Y2 0.029214014 0.018085413 0.08732790 0.09396024
## pubt_Y3 0.003475505 0.005261589 0.05557784 0.08932391
## int_p_ave 0.364357470 0.374932706 0.43735274 0.33937486
## famc_ave 0.171589197 0.179936033 0.19711306 0.20711338
## bw_lbs -0.001175679 0.002893620 0.02063425 0.03825489
## matage -0.102718436 -0.076087353 -0.04501843 -0.02029135
## matalc_ave 0.058322610 0.001763609 0.06017958 0.01933893
## matcig_ave 0.082429791 0.076840869 0.08092605 0.06714336
## matmar_ave 0.067370984 0.049705393 0.05650990 0.03617843
## ses_lt -0.252357578 -0.226078211 -0.21536207 -0.16909885
## wchrt_Baseline wchrt_Y1 wchrt_Y2 wchrt_Y3
## intt_Baseline 0.105940753 0.074114430 0.086126974 0.099129687
## intt_Y1 0.109657195 0.080570086 0.092143054 0.082958874
## intt_Y2 0.095263426 0.062123652 0.102252100 0.146143279
## intt_Y3 0.091501444 0.072483023 0.101497559 0.144944504
## wchrt_Baseline 1.000000000 0.687730943 0.699551424 0.613538842
## wchrt_Y1 0.687730943 1.000000000 0.693349613 0.680766334
## wchrt_Y2 0.699551424 0.693349613 1.000000000 0.770756010
## wchrt_Y3 0.613538842 0.680766334 0.770756010 1.000000000
## pubt_Baseline 0.188744953 0.185593414 0.196047113 0.190781561
## pubt_Y1 0.127989752 0.154885435 0.194990360 0.187332460
## pubt_Y2 0.165373338 0.152801236 0.189591739 0.242513005
## pubt_Y3 0.134455498 0.128615590 0.158267074 0.185818250
## int_p_ave 0.045365277 0.054395603 0.053764225 0.103004712
## famc_ave 0.007981607 -0.000289402 0.005860146 0.042467646
## bw_lbs 0.083393178 0.059053837 0.045280626 0.059989915
## matage -0.108358903 -0.113468780 -0.161692951 -0.195410327
## matalc_ave 0.020911880 0.009641091 0.008752588 -0.047163647
## matcig_ave 0.085390681 0.060934928 0.087513087 0.252788177
## matmar_ave 0.050170891 0.039689510 0.070870741 0.007549731
## ses_lt -0.279389791 -0.246147173 -0.331133704 -0.347660267
## pubt_Baseline pubt_Y1 pubt_Y2 pubt_Y3 int_p_ave
## intt_Baseline 0.067202313 0.04372240 0.02921401 0.003475505 0.36435747
## intt_Y1 0.072375380 0.07704535 0.01808541 0.005261589 0.37493271
## intt_Y2 0.086442827 0.10684269 0.08732790 0.055577840 0.43735274
## intt_Y3 0.070621261 0.10832306 0.09396024 0.089323913 0.33937486
## wchrt_Baseline 0.188744953 0.12798975 0.16537334 0.134455498 0.04536528
## wchrt_Y1 0.185593414 0.15488544 0.15280124 0.128615590 0.05439560
## wchrt_Y2 0.196047113 0.19499036 0.18959174 0.158267074 0.05376422
## wchrt_Y3 0.190781561 0.18733246 0.24251300 0.185818250 0.10300471
## pubt_Baseline 1.000000000 0.46338874 0.34311227 0.243557557 0.06490699
## pubt_Y1 0.463388739 1.00000000 0.57353711 0.443380600 0.02706675
## pubt_Y2 0.343112270 0.57353711 1.00000000 0.561569185 0.03167827
## pubt_Y3 0.243557557 0.44338060 0.56156918 1.000000000 0.01438374
## int_p_ave 0.064906995 0.02706675 0.03167827 0.014383740 1.00000000
## famc_ave -0.027050047 -0.03759675 -0.02268188 -0.047417032 0.33069219
## bw_lbs -0.002937238 0.01844125 -0.02154450 0.004815945 0.03583271
## matage -0.158907175 -0.08056884 -0.10221907 -0.082201357 -0.04142178
## matalc_ave -0.003824003 -0.00656223 0.02348699 0.006002508 0.05125313
## matcig_ave 0.069372283 0.06320936 0.06180227 0.035486193 0.09979529
## matmar_ave 0.032518036 0.05262083 0.03929451 0.022943287 0.08777126
## ses_lt -0.228204452 -0.20254816 -0.18224390 -0.138791809 -0.30098071
## famc_ave bw_lbs matage matalc_ave matcig_ave
## intt_Baseline 0.171589197 -0.001175679 -0.102718436 0.058322610 0.08242979
## intt_Y1 0.179936033 0.002893620 -0.076087353 0.001763609 0.07684087
## intt_Y2 0.197113056 0.020634250 -0.045018429 0.060179578 0.08092605
## intt_Y3 0.207113378 0.038254890 -0.020291353 0.019338933 0.06714336
## wchrt_Baseline 0.007981607 0.083393178 -0.108358903 0.020911880 0.08539068
## wchrt_Y1 -0.000289402 0.059053837 -0.113468780 0.009641091 0.06093493
## wchrt_Y2 0.005860146 0.045280626 -0.161692951 0.008752588 0.08751309
## wchrt_Y3 0.042467646 0.059989915 -0.195410327 -0.047163647 0.25278818
## pubt_Baseline -0.027050047 -0.002937238 -0.158907175 -0.003824003 0.06937228
## pubt_Y1 -0.037596748 0.018441249 -0.080568839 -0.006562230 0.06320936
## pubt_Y2 -0.022681878 -0.021544501 -0.102219067 0.023486991 0.06180227
## pubt_Y3 -0.047417032 0.004815945 -0.082201357 0.006002508 0.03548619
## int_p_ave 0.330692190 0.035832715 -0.041421784 0.051253132 0.09979529
## famc_ave 1.000000000 0.011813744 0.011197830 0.040081117 0.04386867
## bw_lbs 0.011813744 1.000000000 -0.003120232 0.006500121 -0.04214129
## matage 0.011197830 -0.003120232 1.000000000 0.033601539 -0.06339791
## matalc_ave 0.040081117 0.006500121 0.033601539 1.000000000 0.19389421
## matcig_ave 0.043868672 -0.042141294 -0.063397912 0.193894211 1.00000000
## matmar_ave 0.029964344 -0.009808363 -0.086547582 0.071259864 0.13319494
## ses_lt -0.099308103 0.039716175 0.509444691 -0.007411927 -0.22344928
## matmar_ave ses_lt
## intt_Baseline 0.067370984 -0.252357578
## intt_Y1 0.049705393 -0.226078211
## intt_Y2 0.056509897 -0.215362074
## intt_Y3 0.036178429 -0.169098853
## wchrt_Baseline 0.050170891 -0.279389791
## wchrt_Y1 0.039689510 -0.246147173
## wchrt_Y2 0.070870741 -0.331133704
## wchrt_Y3 0.007549731 -0.347660267
## pubt_Baseline 0.032518036 -0.228204452
## pubt_Y1 0.052620830 -0.202548163
## pubt_Y2 0.039294512 -0.182243903
## pubt_Y3 0.022943287 -0.138791809
## int_p_ave 0.087771261 -0.300980711
## famc_ave 0.029964344 -0.099308103
## bw_lbs -0.009808363 0.039716175
## matage -0.086547582 0.509444691
## matalc_ave 0.071259864 -0.007411927
## matcig_ave 0.133194945 -0.223449280
## matmar_ave 1.000000000 -0.156379966
## ses_lt -0.156379966 1.000000000
# Create a ggheatmap for age regressed variables + covariates
ggheatmap <- ggplot(melt(cor_matrix_cov_t), aes(Var2, Var1, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "black", high = "#8e6f3e", mid = "white",
midpoint = 0, limit = c(-1, 1), space = "Lab",
name = "Pearson\nCorrelation") +
theme_minimal() + # minimal theme
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1)) +
coord_fixed()
# Create a ggheatmap with labels on the lower triangle
ggheatmap +
geom_text(aes(Var1, Var2, label = ifelse(as.numeric(Var1) > as.numeric(Var2), round(value, 2), "")),
color = "black", size = 2) +
theme(
axis.title.x = element_blank(),
axis.title.y = element_blank(),
panel.grid.major = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.ticks = element_blank(),
legend.justification = c(1, 0),
legend.position = c(1.3, 0.1),
legend.direction = "vertical"
) +
guides(fill = guide_colorbar(barwidth = 1.3, barheight = 6,
title.position = "top", title.hjust = 0.5)) +
coord_fixed() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
## Coordinate system already present. Adding new coordinate system, which will
## replace the existing one.
# Subset data set for mplus
all_merged_out <- all_merged[, c("sex","race3", "age_Baseline",
"int_Baseline", "intt_Baseline",
"age_Y1", "int_Y1", "intt_Y1",
"age_Y2", "int_Y2", "intt_Y2",
"age_Y3", "int_Y3", "intt_Y3",
"wchr_Baseline", "wchrt_Baseline",
"wchr_Y1", "wchrt_Y1",
"wchr_Y2", "wchrt_Y2",
"wchr_Y3", "wchrt_Y3",
"pub_Baseline", "pubt_Baseline",
"pub_Y1", "pubt_Y1",
"pub_Y2", "pubt_Y2",
"pub_Y3", "pubt_Y3",
"int_p_ave", "famc_ave", "bw_lbs",
"matage", "matalc_ave", "matcig_ave",
"matmar_ave", "ses_lt")]
#install.packages("MplusAutomation")
library(MplusAutomation)
## Warning: package 'MplusAutomation' was built under R version 4.3.3
## Version: 1.1.1
## We work hard to write this free software. Please help us get credit by citing:
##
## Hallquist, M. N. & Wiley, J. F. (2018). MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus. Structural Equation Modeling, 25, 621-638. doi: 10.1080/10705511.2017.1402334.
##
## -- see citation("MplusAutomation").
# Specify the output Mplus data file name (e.g., "data_input.dat")
mplus_data_file <- "data_input.dat"
# Export data to Mplus input format
MplusAutomation::prepareMplusData(all_merged_out,filename = "Fullsample_race3_sex_subset.dat")
## The file(s)
## 'Fullsample_race3_sex_subset.dat'
## currently exist(s) and will be overwritten
## TITLE: Your title goes here
## DATA: FILE = "Fullsample_race3_sex_subset.dat";
## VARIABLE:
## NAMES = sex race3 age_Baseline int_Baseline intt_Baseline age_Y1 int_Y1 intt_Y1
## age_Y2 int_Y2 intt_Y2 age_Y3 int_Y3 intt_Y3 wchr_Baseline wchrt_Baseline
## wchr_Y1 wchrt_Y1 wchr_Y2 wchrt_Y2 wchr_Y3 wchrt_Y3 pub_Baseline pubt_Baseline
## pub_Y1 pubt_Y1 pub_Y2 pubt_Y2 pub_Y3 pubt_Y3 int_p_ave famc_ave bw_lbs matage
## matalc_ave matcig_ave matmar_ave ses_lt;
## MISSING=.;
Hallquist, M. N. & Wiley, J. F. (2018). MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus. Structural Equation Modeling, 25, 621-638. doi: 10.1080/10705511.2017.1402334.
# Specify the path to your Mplus output file
output_path <- "C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Obesity Week/Mplus/internalizing unconstrained.out"
# Read and print the file
output_content <- readLines(output_path)
cat(output_content, sep = "\n")
## Mplus VERSION 8.10
## MUTHEN & MUTHEN
## 07/01/2024 11:59 AM
##
## INPUT INSTRUCTIONS
##
## TITLE: Internalizing Constrained Model
## DATA: FILE = "Fullsample_race3_sex_subset.dat";
## VARIABLE:
## NAMES = sex race3 age_B int_B intt_B age_1 int_1 intt_1
## age_2 int_2 intt_2 age_3 int_3 intt_3 wchr_B wchrt_B
## wchr_1 wchrt_1 wchr_2 wchrt_2 wchr_3 wchrt_3 pub_B pubt_B
## pub_1 pubt_1 pub_2 pubt_2 pub_3 pubt_3 int_p_ave famc_ave bw_lbs matage
## matalc_ave matcig_ave matmar_ave ses_lt;
## MISSING=.;
##
## USEVARIABLES ARE
## intt_b intt_1
## intt_2 intt_3;
##
## ANALYSIS:
## ESTIMATOR=MLR;
## MODEL=NOCOVARIANCES;
##
## MODEL:
## ! Estimate random inttercept)
## RI_intt BY intt_b@1 intt_1@1 intt_2@1 intt_3@1;
##
## ! Create within-person centered variables
## wintt_b BY intt_b@1;
## wintt_1 BY intt_1@1;
## wintt_2 BY intt_2@1;
## wintt_3 BY intt_3@1;
##
## ! Estimate the Lagged Effects
## wintt_1 ON wintt_b;
## wintt_2 ON wintt_1;
## wintt_3 ON wintt_2;
##
## ! Constrain the measurement error variances to zero
## intt_b@0;
## intt_1@0;
## intt_2@0;
## intt_3@0;
##
## ! ask for variances for all variables that are included;
## [intt_b intt_1 intt_2 intt_3];
##
## ! Fix the correlation between the individual factors and the other
## ! exogenous variables to zero (by default these would be estimated)
## RI_intt with wintt_b@0;
##
## OUTPUT: STDYX MODINDICES;
##
##
##
## *** WARNING
## Data set contains cases with missing on all variables.
## These cases were not included in the analysis.
## Number of cases with missing on all variables: 2095
## 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
##
##
##
## Internalizing Constrained Model
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 10361
##
## Number of dependent variables 4
## Number of independent variables 0
## Number of continuous latent variables 5
##
## Observed dependent variables
##
## Continuous
## INTT_B INTT_1 INTT_2 INTT_3
##
## Continuous latent variables
## RI_INTT WINTT_B WINTT_1 WINTT_2 WINTT_3
##
##
## Estimator MLR
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Fullsample_race3_sex_subset.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 12
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## INTT_B INTT_1 INTT_2 INTT_3
## ________ ________ ________ ________
## INTT_B 0.999
## INTT_1 0.943 0.944
## INTT_2 0.918 0.897 0.919
## INTT_3 0.851 0.833 0.834 0.851
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## INTT_B 0.000 2.512 -1.161 0.27% -1.096 -0.934 -0.912
## 10355.000 2.893 8.153 13.915 0.01% -0.129 0.904
## INTT_1 0.000 2.416 -1.298 0.01% -1.130 -1.052 -0.986
## 9780.000 3.147 7.265 12.912 0.01% -0.139 0.905
## INTT_2 0.000 2.363 -1.697 0.01% -1.274 -1.119 -1.024
## 9519.000 3.840 7.067 15.034 0.01% -0.293 0.867
## INTT_3 0.000 2.123 -1.822 0.02% -1.459 -1.307 -0.672
## 8819.000 4.614 5.379 13.693 0.01% -0.398 1.370
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 12
##
## Loglikelihood
##
## H0 Value -71300.350
## H0 Scaling Correction Factor 2.8090
## for MLR
## H1 Value -71257.871
## H1 Scaling Correction Factor 2.7385
## for MLR
##
## Information Criteria
##
## Akaike (AIC) 142624.700
## Bayesian (BIC) 142711.649
## Sample-Size Adjusted BIC 142673.515
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 36.694*
## Degrees of Freedom 2
## P-Value 0.0000
## Scaling Correction Factor 2.3153
## for MLR
##
## * The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
## for chi-square difference testing in the regular way. MLM, MLR and WLSM
## chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
## and ULSMV difference testing is done using the DIFFTEST option.
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.041
## 90 Percent C.I. 0.030 0.053
## Probability RMSEA <= .05 0.888
##
## CFI/TLI
##
## CFI 0.994
## TLI 0.981
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 5547.969
## Degrees of Freedom 6
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.016
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_INTT BY
## INTT_B 1.000 0.000 999.000 999.000
## INTT_1 1.000 0.000 999.000 999.000
## INTT_2 1.000 0.000 999.000 999.000
## INTT_3 1.000 0.000 999.000 999.000
##
## WINTT_B BY
## INTT_B 1.000 0.000 999.000 999.000
##
## WINTT_1 BY
## INTT_1 1.000 0.000 999.000 999.000
##
## WINTT_2 BY
## INTT_2 1.000 0.000 999.000 999.000
##
## WINTT_3 BY
## INTT_3 1.000 0.000 999.000 999.000
##
## WINTT_1 ON
## WINTT_B 0.053 0.047 1.130 0.259
##
## WINTT_2 ON
## WINTT_1 0.230 0.037 6.285 0.000
##
## WINTT_3 ON
## WINTT_2 0.427 0.026 16.718 0.000
##
## RI_INTT WITH
## WINTT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## INTT_B 0.001 0.017 0.041 0.967
## INTT_1 0.008 0.018 0.475 0.635
## INTT_2 0.010 0.020 0.496 0.620
## INTT_3 0.024 0.023 1.056 0.291
##
## Variances
## RI_INTT 1.848 0.070 26.366 0.000
## WINTT_B 1.113 0.066 16.749 0.000
##
## Residual Variances
## INTT_B 0.000 0.000 999.000 999.000
## INTT_1 0.000 0.000 999.000 999.000
## INTT_2 0.000 0.000 999.000 999.000
## INTT_3 0.000 0.000 999.000 999.000
## WINTT_1 1.231 0.075 16.493 0.000
## WINTT_2 1.958 0.076 25.677 0.000
## WINTT_3 2.463 0.079 31.047 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.318E-01
## (ratio of smallest to largest eigenvalue)
##
##
## STANDARDIZED MODEL RESULTS
##
##
## STDYX Standardization
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_INTT BY
## INTT_B 0.790 0.011 73.106 0.000
## INTT_1 0.774 0.013 60.711 0.000
## INTT_2 0.691 0.010 67.973 0.000
## INTT_3 0.628 0.010 63.799 0.000
##
## WINTT_B BY
## INTT_B 0.613 0.014 44.024 0.000
##
## WINTT_1 BY
## INTT_1 0.633 0.016 40.539 0.000
##
## WINTT_2 BY
## INTT_2 0.723 0.010 74.450 0.000
##
## WINTT_3 BY
## INTT_3 0.778 0.008 97.788 0.000
##
## WINTT_1 ON
## WINTT_B 0.050 0.044 1.138 0.255
##
## WINTT_2 ON
## WINTT_1 0.179 0.030 5.992 0.000
##
## WINTT_3 ON
## WINTT_2 0.361 0.021 17.576 0.000
##
## RI_INTT WITH
## WINTT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## INTT_B 0.000 0.010 0.041 0.967
## INTT_1 0.005 0.010 0.478 0.633
## INTT_2 0.005 0.010 0.499 0.618
## INTT_3 0.011 0.010 1.067 0.286
##
## Variances
## RI_INTT 1.000 0.000 999.000 999.000
## WINTT_B 1.000 0.000 999.000 999.000
##
## Residual Variances
## INTT_B 0.000 999.000 999.000 999.000
## INTT_1 0.000 999.000 999.000 999.000
## INTT_2 0.000 999.000 999.000 999.000
## INTT_3 0.000 999.000 999.000 999.000
## WINTT_1 0.997 0.004 225.603 0.000
## WINTT_2 0.968 0.011 90.006 0.000
## WINTT_3 0.870 0.015 58.703 0.000
##
##
## R-SQUARE
##
## Observed Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## INTT_B 1.000 999.000 999.000 999.000
## INTT_1 1.000 999.000 999.000 999.000
## INTT_2 1.000 999.000 999.000 999.000
## INTT_3 1.000 999.000 999.000 999.000
##
## Latent Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WINTT_1 0.003 0.004 0.569 0.569
## WINTT_2 0.032 0.011 2.996 0.003
## WINTT_3 0.130 0.015 8.788 0.000
##
##
## MODEL MODIFICATION INDICES
##
## NOTE: Modification indices for direct effects of observed dependent variables
## regressed on covariates may not be included. To include these, request
## MODINDICES (ALL).
##
## Minimum M.I. value for printing the modification index 10.000
##
## M.I. E.P.C. Std E.P.C. StdYX E.P.C.
##
## BY Statements
##
## RI_INTT BY INTT_B 32.952 -0.236 -0.321 -0.187
## RI_INTT BY INTT_1 28.314 0.175 0.238 0.135
## WINTT_B BY INTT_3 23.596 -0.174 -0.183 -0.085
## WINTT_1 BY INTT_2 32.940 -0.443 -0.492 -0.250
## WINTT_1 BY INTT_3 33.005 0.189 0.210 0.097
## WINTT_3 BY INTT_B 30.408 -0.095 -0.159 -0.093
## WINTT_3 BY INTT_1 33.506 0.089 0.150 0.086
## WINTT_3 BY INTT_2 33.057 -0.656 -1.103 -0.561
##
## ON/BY Statements
##
## RI_INTT ON WINTT_B /
## WINTT_B BY RI_INTT 32.943 -0.417 -0.324 -0.324
## RI_INTT ON WINTT_1 /
## WINTT_1 BY RI_INTT 33.096 0.331 0.270 0.270
## WINTT_B ON RI_INTT /
## RI_INTT BY WINTT_B 32.943 -0.251 -0.324 -0.324
## WINTT_B ON WINTT_3 /
## WINTT_3 BY WINTT_B 29.463 -0.098 -0.156 -0.156
## WINTT_1 ON RI_INTT /
## RI_INTT BY WINTT_1 33.090 0.212 0.259 0.259
## WINTT_1 ON WINTT_3 /
## WINTT_3 BY WINTT_1 33.516 0.092 0.140 0.140
## WINTT_2 ON WINTT_3 /
## WINTT_3 BY WINTT_2 33.160 -0.657 -0.777 -0.777
## WINTT_3 ON WINTT_B /
## WINTT_B BY WINTT_3 23.596 -0.174 -0.109 -0.109
## WINTT_3 ON WINTT_1 /
## WINTT_1 BY WINTT_3 33.005 0.189 0.125 0.125
##
## WITH Statements
##
## INTT_2 WITH INTT_1 34.466 -0.532 -0.532 999.000
## INTT_3 WITH INTT_B 24.903 -0.192 -0.192 999.000
## INTT_3 WITH INTT_1 33.641 0.221 0.221 999.000
## INTT_3 WITH INTT_2 32.835 -1.610 -1.610 999.000
## WINTT_B WITH RI_INTT 32.943 -0.464 -0.324 -0.324
## WINTT_1 WITH RI_INTT 33.090 0.391 0.259 0.259
## WINTT_3 WITH WINTT_B 23.596 -0.193 -0.117 -0.117
## WINTT_3 WITH WINTT_1 33.662 0.228 0.131 0.131
## WINTT_3 WITH WINTT_2 32.945 -1.612 -0.734 -0.734
##
## Variances/Residual Variances
##
## INTT_2 32.124 3.730 3.730 0.963
##
##
## DIAGRAM INFORMATION
##
## Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram.
## If running Mplus from the Mplus Diagrammer, the diagram opens automatically.
##
## Diagram output
## c:\users\ocrobert\onedrive - indiana university\abcd\obesity week\mplus\internalizing unconstrained.dgm
##
## Beginning Time: 11:59:19
## Ending Time: 11:59:19
## Elapsed Time: 00:00:00
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2023 Muthen & Muthen
# Specify the path to your Mplus output file
output_path <- "C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Obesity Week/Mplus/internalizing constrained.out"
# Read and print the file
output_content <- readLines(output_path)
cat(output_content, sep = "\n")
## Mplus VERSION 8.10
## MUTHEN & MUTHEN
## 07/01/2024 11:50 AM
##
## INPUT INSTRUCTIONS
##
## TITLE: Internalizing Constrained Model
## DATA: FILE = "Fullsample_race3_sex_subset.dat";
## VARIABLE:
## NAMES = sex race3 age_B int_B intt_B age_1 int_1 intt_1
## age_2 int_2 intt_2 age_3 int_3 intt_3 wchr_B wchrt_B
## wchr_1 wchrt_1 wchr_2 wchrt_2 wchr_3 wchrt_3 pub_B pubt_B
## pub_1 pubt_1 pub_2 pubt_2 pub_3 pubt_3 int_p_ave famc_ave bw_lbs matage
## matalc_ave matcig_ave matmar_ave ses_lt;
## MISSING=.;
##
## USEVARIABLES ARE
## intt_b intt_1
## intt_2 intt_3;
##
## ANALYSIS:
## ESTIMATOR=MLR;
## MODEL=NOCOVARIANCES;
##
## MODEL:
## ! Estimate random inttercept)
## RI_intt BY intt_b@1 intt_1@1 intt_2@1 intt_3@1;
##
## ! Create within-person centered variables
## wintt_b BY intt_b@1;
## wintt_1 BY intt_1@1;
## wintt_2 BY intt_2@1;
## wintt_3 BY intt_3@1;
##
## ! Estimate the Lagged Effects
## wintt_1 ON wintt_b (1);
## wintt_2 ON wintt_1 (1);
## wintt_3 ON wintt_2 (1);
##
## ! Constrain the measurement error variances to zero
## intt_b@0;
## intt_1@0;
## intt_2@0;
## intt_3@0;
##
## ! ask for variances for all variables that are included;
## [intt_b intt_1 intt_2 intt_3];
##
## ! Fix the correlation between the individual factors and the other
## ! exogenous variables to zero (by default these would be estimated)
## RI_intt with wintt_b@0;
##
## OUTPUT: STDYX MODINDICES;
##
##
##
## *** WARNING
## Data set contains cases with missing on all variables.
## These cases were not included in the analysis.
## Number of cases with missing on all variables: 2095
## 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
##
##
##
## Internalizing Constrained Model
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 10361
##
## Number of dependent variables 4
## Number of independent variables 0
## Number of continuous latent variables 5
##
## Observed dependent variables
##
## Continuous
## INTT_B INTT_1 INTT_2 INTT_3
##
## Continuous latent variables
## RI_INTT WINTT_B WINTT_1 WINTT_2 WINTT_3
##
##
## Estimator MLR
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Fullsample_race3_sex_subset.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 12
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## INTT_B INTT_1 INTT_2 INTT_3
## ________ ________ ________ ________
## INTT_B 0.999
## INTT_1 0.943 0.944
## INTT_2 0.918 0.897 0.919
## INTT_3 0.851 0.833 0.834 0.851
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## INTT_B 0.000 2.512 -1.161 0.27% -1.096 -0.934 -0.912
## 10355.000 2.893 8.153 13.915 0.01% -0.129 0.904
## INTT_1 0.000 2.416 -1.298 0.01% -1.130 -1.052 -0.986
## 9780.000 3.147 7.265 12.912 0.01% -0.139 0.905
## INTT_2 0.000 2.363 -1.697 0.01% -1.274 -1.119 -1.024
## 9519.000 3.840 7.067 15.034 0.01% -0.293 0.867
## INTT_3 0.000 2.123 -1.822 0.02% -1.459 -1.307 -0.672
## 8819.000 4.614 5.379 13.693 0.01% -0.398 1.370
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 10
##
## Loglikelihood
##
## H0 Value -71389.816
## H0 Scaling Correction Factor 2.8983
## for MLR
## H1 Value -71257.871
## H1 Scaling Correction Factor 2.7385
## for MLR
##
## Information Criteria
##
## Akaike (AIC) 142799.631
## Bayesian (BIC) 142872.089
## Sample-Size Adjusted BIC 142840.311
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 112.830*
## Degrees of Freedom 4
## P-Value 0.0000
## Scaling Correction Factor 2.3388
## for MLR
##
## * The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
## for chi-square difference testing in the regular way. MLM, MLR and WLSM
## chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
## and ULSMV difference testing is done using the DIFFTEST option.
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.051
## 90 Percent C.I. 0.043 0.060
## Probability RMSEA <= .05 0.383
##
## CFI/TLI
##
## CFI 0.980
## TLI 0.971
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 5547.969
## Degrees of Freedom 6
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.038
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_INTT BY
## INTT_B 1.000 0.000 999.000 999.000
## INTT_1 1.000 0.000 999.000 999.000
## INTT_2 1.000 0.000 999.000 999.000
## INTT_3 1.000 0.000 999.000 999.000
##
## WINTT_B BY
## INTT_B 1.000 0.000 999.000 999.000
##
## WINTT_1 BY
## INTT_1 1.000 0.000 999.000 999.000
##
## WINTT_2 BY
## INTT_2 1.000 0.000 999.000 999.000
##
## WINTT_3 BY
## INTT_3 1.000 0.000 999.000 999.000
##
## WINTT_1 ON
## WINTT_B 0.317 0.029 11.042 0.000
##
## WINTT_2 ON
## WINTT_1 0.317 0.029 11.042 0.000
##
## WINTT_3 ON
## WINTT_2 0.317 0.029 11.042 0.000
##
## RI_INTT WITH
## WINTT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## INTT_B 0.001 0.017 0.042 0.967
## INTT_1 0.009 0.018 0.516 0.606
## INTT_2 0.009 0.020 0.473 0.636
## INTT_3 0.021 0.022 0.951 0.342
##
## Variances
## RI_INTT 1.670 0.072 23.087 0.000
## WINTT_B 1.349 0.061 21.995 0.000
##
## Residual Variances
## INTT_B 0.000 0.000 999.000 999.000
## INTT_1 0.000 0.000 999.000 999.000
## INTT_2 0.000 0.000 999.000 999.000
## INTT_3 0.000 0.000 999.000 999.000
## WINTT_1 1.495 0.067 22.388 0.000
## WINTT_2 1.926 0.078 24.722 0.000
## WINTT_3 2.392 0.081 29.545 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.399E-01
## (ratio of smallest to largest eigenvalue)
##
##
## STANDARDIZED MODEL RESULTS
##
##
## STDYX Standardization
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_INTT BY
## INTT_B 0.744 0.010 75.559 0.000
## INTT_1 0.711 0.014 52.098 0.000
## INTT_2 0.666 0.014 48.870 0.000
## INTT_3 0.625 0.013 49.313 0.000
##
## WINTT_B BY
## INTT_B 0.668 0.011 61.009 0.000
##
## WINTT_1 BY
## INTT_1 0.703 0.014 50.851 0.000
##
## WINTT_2 BY
## INTT_2 0.746 0.012 61.156 0.000
##
## WINTT_3 BY
## INTT_3 0.780 0.010 76.821 0.000
##
## WINTT_1 ON
## WINTT_B 0.288 0.021 13.799 0.000
##
## WINTT_2 ON
## WINTT_1 0.280 0.027 10.374 0.000
##
## WINTT_3 ON
## WINTT_2 0.284 0.028 10.275 0.000
##
## RI_INTT WITH
## WINTT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## INTT_B 0.000 0.010 0.042 0.967
## INTT_1 0.005 0.010 0.519 0.604
## INTT_2 0.005 0.010 0.476 0.634
## INTT_3 0.010 0.011 0.959 0.337
##
## Variances
## RI_INTT 1.000 0.000 999.000 999.000
## WINTT_B 1.000 0.000 999.000 999.000
##
## Residual Variances
## INTT_B 0.000 999.000 999.000 999.000
## INTT_1 0.000 999.000 999.000 999.000
## INTT_2 0.000 999.000 999.000 999.000
## INTT_3 0.000 999.000 999.000 999.000
## WINTT_1 0.917 0.012 76.105 0.000
## WINTT_2 0.922 0.015 60.995 0.000
## WINTT_3 0.919 0.016 58.509 0.000
##
##
## R-SQUARE
##
## Observed Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## INTT_B 1.000 999.000 999.000 999.000
## INTT_1 1.000 999.000 999.000 999.000
## INTT_2 1.000 999.000 999.000 999.000
## INTT_3 1.000 999.000 999.000 999.000
##
## Latent Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WINTT_1 0.083 0.012 6.899 0.000
## WINTT_2 0.078 0.015 5.187 0.000
## WINTT_3 0.081 0.016 5.137 0.000
##
##
## MODEL MODIFICATION INDICES
##
## NOTE: Modification indices for direct effects of observed dependent variables
## regressed on covariates may not be included. To include these, request
## MODINDICES (ALL).
##
## Minimum M.I. value for printing the modification index 10.000
##
## M.I. E.P.C. Std E.P.C. StdYX E.P.C.
##
## BY Statements
##
## RI_INTT BY INTT_B 40.049 -0.215 -0.278 -0.160
## RI_INTT BY INTT_3 28.640 0.143 0.185 0.090
## WINTT_B BY INTT_B 39.577 0.592 0.688 0.396
## WINTT_B BY INTT_1 43.522 -0.182 -0.211 -0.116
## WINTT_B BY INTT_2 14.157 0.110 0.128 0.066
## WINTT_B BY INTT_3 18.145 -0.151 -0.175 -0.085
## WINTT_1 BY INTT_B 39.574 -0.186 -0.238 -0.137
## WINTT_1 BY INTT_3 70.636 0.236 0.301 0.146
## WINTT_2 BY INTT_2 17.602 -0.194 -0.280 -0.144
## WINTT_2 BY INTT_3 53.146 0.214 0.309 0.149
## WINTT_3 BY INTT_B 41.351 -0.116 -0.187 -0.108
## WINTT_3 BY INTT_1 44.163 0.099 0.160 0.088
## WINTT_3 BY INTT_2 22.452 0.139 0.223 0.115
## WINTT_3 BY INTT_3 53.683 0.678 1.093 0.529
##
## ON/BY Statements
##
## RI_INTT ON WINTT_B /
## WINTT_B BY RI_INTT 67.105 -0.409 -0.367 -0.367
## RI_INTT ON WINTT_2 /
## WINTT_2 BY RI_INTT 12.525 0.087 0.097 0.097
## RI_INTT ON WINTT_3 /
## WINTT_3 BY RI_INTT 43.517 0.131 0.164 0.164
## WINTT_B ON RI_INTT /
## RI_INTT BY WINTT_B 67.105 -0.330 -0.367 -0.367
## WINTT_B ON WINTT_1 /
## WINTT_1 BY WINTT_B 39.588 -0.169 -0.186 -0.186
## WINTT_B ON WINTT_3 /
## WINTT_3 BY WINTT_B 22.236 -0.114 -0.158 -0.158
## WINTT_1 ON WINTT_B /
## WINTT_B BY WINTT_1 39.587 -0.145 -0.132 -0.132
## WINTT_1 ON WINTT_1 /
## WINTT_1 BY WINTT_1 39.730 -0.594 -0.594 -0.594
## WINTT_1 ON WINTT_3 /
## WINTT_3 BY WINTT_1 62.616 0.124 0.156 0.156
## WINTT_2 ON RI_INTT /
## RI_INTT BY WINTT_2 14.523 0.095 0.085 0.085
## WINTT_2 ON WINTT_B /
## WINTT_B BY WINTT_2 10.951 0.121 0.097 0.097
## WINTT_2 ON WINTT_3 /
## WINTT_3 BY WINTT_2 25.043 0.134 0.149 0.149
## WINTT_3 ON RI_INTT /
## RI_INTT BY WINTT_3 28.640 0.143 0.115 0.115
## WINTT_3 ON WINTT_B /
## WINTT_B BY WINTT_3 18.145 -0.151 -0.109 -0.109
## WINTT_3 ON WINTT_1 /
## WINTT_1 BY WINTT_3 70.636 0.236 0.187 0.187
## WINTT_3 ON WINTT_2 /
## WINTT_2 BY WINTT_3 53.146 0.092 0.082 0.082
## WINTT_3 ON WINTT_3 /
## WINTT_3 BY WINTT_3 53.683 0.678 0.678 0.678
##
## WITH Statements
##
## INTT_1 WITH INTT_B 50.030 -0.275 -0.275 999.000
## INTT_2 WITH INTT_B 19.911 0.150 0.150 999.000
## INTT_2 WITH INTT_1 13.686 -0.127 -0.127 999.000
## INTT_3 WITH INTT_B 37.309 -0.232 -0.232 999.000
## INTT_3 WITH INTT_1 55.236 0.276 0.276 999.000
## INTT_3 WITH INTT_2 22.758 0.285 0.285 999.000
## WINTT_B WITH RI_INTT 67.105 -0.551 -0.367 -0.367
## WINTT_1 WITH WINTT_B 39.587 -0.253 -0.178 -0.178
## WINTT_2 WITH RI_INTT 14.523 0.159 0.089 0.089
## WINTT_2 WITH WINTT_B 10.951 0.163 0.101 0.101
## WINTT_3 WITH RI_INTT 28.640 0.239 0.120 0.120
## WINTT_3 WITH WINTT_B 18.145 -0.203 -0.113 -0.113
## WINTT_3 WITH WINTT_1 73.031 0.324 0.171 0.171
## WINTT_3 WITH WINTT_2 22.789 0.285 0.133 0.133
##
## Variances/Residual Variances
##
## INTT_B 39.576 0.799 0.799 0.265
## INTT_2 22.826 -0.901 -0.901 -0.240
##
##
## DIAGRAM INFORMATION
##
## Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram.
## If running Mplus from the Mplus Diagrammer, the diagram opens automatically.
##
## Diagram output
## c:\users\ocrobert\onedrive - indiana university\abcd\obesity week\mplus\internalizing constrained.dgm
##
## Beginning Time: 11:50:55
## Ending Time: 11:50:55
## Elapsed Time: 00:00:00
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2023 Muthen & Muthen
https://www.statmodel.com/chidiff.shtml
#install.packages("stats")
library(stats)
# Number of Free Parameters 12
# Loglikelihood
# H0 Value -71300.350
# H0 Scaling Correction Factor 2.8090
# for MLR
# H1 Value -71257.871
# H1 Scaling Correction Factor 2.7385
# for MLR
# Unconstrained
L1 <- -71300.350 # Loglikelihood for the H1 model
c1 <- 2.8090 # Scaling correction factor for the H1 model
p1 <- 12 # Number of parameters for the H1 model
# Number of Free Parameters 10
# Loglikelihood
# H0 Value -71389.816
# H0 Scaling Correction Factor 2.8983
# for MLR
# H1 Value -71257.871
# H1 Scaling Correction Factor 2.7385
# for MLR
# Constrained
L0 <- -71389.816 # Loglikelihood for the H0 model
c0 <- 2.8983 # Scaling correction factor for the H0 model
p0 <- 10 # Number of parameters for the H0 model
# Compute the difference test scaling correction (cd)
cd <- (p0 * c0 - p1 * c1) / (p0 - p1)
# Compute the chi-square difference test (TRd)
TRd <- -2 * (L0 - L1) / cd
# Define degrees of freedom
df <- p1 - p0
# Calculate p-value
p_value <- pchisq(TRd, df = df, lower.tail = FALSE)
# Print results
cat("Difference Test Scaling Correction (cd):", cd, "\n")
## Difference Test Scaling Correction (cd): 2.3625
cat("Chi-Square Difference Test (TRd):", TRd, "\n")
## Chi-Square Difference Test (TRd): 75.73841
cat("p-value:", p_value, "\n")
## p-value: 3.577772e-17
# Print "Cannot constrain" if p-value is less than 0.05
if (p_value < 0.05) {
cat("Cannot constrain\n")
} else {
cat("Constraining is acceptable\n")
}
## Cannot constrain
# Specify the path to your Mplus output file
output_path <- "C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Obesity Week/Mplus/wchr unconstrained.out"
# Read and print the file
output_content <- readLines(output_path)
cat(output_content, sep = "\n")
## Mplus VERSION 8.10
## MUTHEN & MUTHEN
## 07/01/2024 12:36 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Waist Circumference to Height Ratio Unconstrained Model
## DATA: FILE = "Fullsample_race3_sex_subset.dat";
## VARIABLE:
## NAMES = sex race3 age_B int_B intt_B age_1 int_1 intt_1
## age_2 int_2 intt_2 age_3 int_3 intt_3 wchr_B wchrt_B
## wchr_1 wchrt_1 wchr_2 wchrt_2 wchr_3 wchrt_3 pub_B pubt_B
## pub_1 pubt_1 pub_2 pubt_2 pub_3 pubt_3 int_p_ave famc_ave bw_lbs matage
## matalc_ave matcig_ave matmar_ave ses_lt;
## MISSING=.;
##
## USEVARIABLES ARE
## wchrt_b wchrt_1
## wchrt_2 wchrt_3;
##
## ANALYSIS:
## ESTIMATOR=MLR;
## MODEL=NOCOVARIANCES;
##
## MODEL:
## ! Estimate random wchrtercept)
## RI_wchrt BY wchrt_b@1 wchrt_1@1 wchrt_2@1 wchrt_3@1;
##
## ! Create within-person centered variables
## wwchrt_b BY wchrt_b@1;
## wwchrt_1 BY wchrt_1@1;
## wwchrt_2 BY wchrt_2@1;
## wwchrt_3 BY wchrt_3@1;
##
## ! Estimate the Lagged Effects
## wwchrt_1 ON wwchrt_b;
## wwchrt_2 ON wwchrt_1;
## wwchrt_3 ON wwchrt_2;
##
## ! Constrain the measurement error variances to zero
## wchrt_b@0;
## wchrt_1@0;
## wchrt_2@0;
## wchrt_3@0;
##
## ! ask for variances for all variables that are included;
## [wchrt_b wchrt_1 wchrt_2 wchrt_3];
##
## ! Fix the correlation between the individual factors and the other
## ! exogenous variables to zero (by default these would be estimated)
## RI_wchrt with wwchrt_b@0;
##
## OUTPUT: STDYX MODINDICES;
##
##
##
## *** WARNING
## Data set contains cases with missing on all variables.
## These cases were not included in the analysis.
## Number of cases with missing on all variables: 2095
## 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
##
##
##
## Waist Circumference to Height Ratio Unconstrained Model
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 10361
##
## Number of dependent variables 4
## Number of independent variables 0
## Number of continuous latent variables 5
##
## Observed dependent variables
##
## Continuous
## WCHRT_B WCHRT_1 WCHRT_2 WCHRT_3
##
## Continuous latent variables
## RI_WCHRT WWCHRT_B WWCHRT_1 WWCHRT_2 WWCHRT_3
##
##
## Estimator MLR
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Fullsample_race3_sex_subset.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 13
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## WCHRT_B WCHRT_1 WCHRT_2 WCHRT_3
## ________ ________ ________ ________
## WCHRT_B 0.998
## WCHRT_1 0.936 0.937
## WCHRT_2 0.763 0.746 0.764
## WCHRT_3 0.169 0.166 0.153 0.169
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## WCHRT_B 0.000 1.308 -0.241 0.01% -0.055 -0.029 -0.014
## 10342.000 0.005 4.917 0.891 0.01% 0.002 0.051
## WCHRT_1 0.000 2.054 -0.213 0.01% -0.058 -0.031 -0.016
## 9711.000 0.006 13.985 0.997 0.01% 0.001 0.054
## WCHRT_2 0.000 1.516 -0.220 0.01% -0.062 -0.033 -0.017
## 7917.000 0.006 6.773 0.922 0.01% 0.002 0.056
## WCHRT_3 0.000 1.723 -0.149 0.06% -0.068 -0.038 -0.022
## 1754.000 0.007 7.313 0.770 0.06% -0.001 0.064
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 12
##
## Loglikelihood
##
## H0 Value 42900.924
## H0 Scaling Correction Factor 8.5361
## for MLR
## H1 Value 42910.217
## H1 Scaling Correction Factor 7.7273
## for MLR
##
## Information Criteria
##
## Akaike (AIC) -85777.849
## Bayesian (BIC) -85690.899
## Sample-Size Adjusted BIC -85729.033
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 6.465*
## Degrees of Freedom 2
## P-Value 0.0395
## Scaling Correction Factor 2.8748
## for MLR
##
## * The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
## for chi-square difference testing in the regular way. MLM, MLR and WLSM
## chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
## and ULSMV difference testing is done using the DIFFTEST option.
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.015
## 90 Percent C.I. 0.003 0.028
## Probability RMSEA <= .05 1.000
##
## CFI/TLI
##
## CFI 0.996
## TLI 0.988
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 1123.303
## Degrees of Freedom 6
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.010
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_WCHRT BY
## WCHRT_B 1.000 0.000 999.000 999.000
## WCHRT_1 1.000 0.000 999.000 999.000
## WCHRT_2 1.000 0.000 999.000 999.000
## WCHRT_3 1.000 0.000 999.000 999.000
##
## WWCHRT_B BY
## WCHRT_B 1.000 0.000 999.000 999.000
##
## WWCHRT_1 BY
## WCHRT_1 1.000 0.000 999.000 999.000
##
## WWCHRT_2 BY
## WCHRT_2 1.000 0.000 999.000 999.000
##
## WWCHRT_3 BY
## WCHRT_3 1.000 0.000 999.000 999.000
##
## WWCHRT_1 ON
## WWCHRT_B -0.114 0.070 -1.628 0.104
##
## WWCHRT_2 ON
## WWCHRT_1 0.200 0.039 5.166 0.000
##
## WWCHRT_3 ON
## WWCHRT_2 0.494 0.078 6.337 0.000
##
## RI_WCHRT WITH
## WWCHRT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## WCHRT_B 0.000 0.001 0.038 0.969
## WCHRT_1 0.001 0.001 0.696 0.486
## WCHRT_2 0.002 0.001 1.939 0.053
## WCHRT_3 -0.007 0.001 -5.158 0.000
##
## Variances
## RI_WCHRT 0.004 0.000 40.241 0.000
## WWCHRT_B 0.001 0.000 8.782 0.000
##
## Residual Variances
## WCHRT_B 0.000 0.000 999.000 999.000
## WCHRT_1 0.000 0.000 999.000 999.000
## WCHRT_2 0.000 0.000 999.000 999.000
## WCHRT_3 0.000 0.000 999.000 999.000
## WWCHRT_1 0.002 0.000 7.539 0.000
## WWCHRT_2 0.002 0.000 12.647 0.000
## WWCHRT_3 0.002 0.000 6.482 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.185E-06
## (ratio of smallest to largest eigenvalue)
##
##
## STANDARDIZED MODEL RESULTS
##
##
## STDYX Standardization
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_WCHRT BY
## WCHRT_B 0.891 0.011 78.966 0.000
## WCHRT_1 0.830 0.018 47.341 0.000
## WCHRT_2 0.797 0.012 65.213 0.000
## WCHRT_3 0.752 0.025 30.468 0.000
##
## WWCHRT_B BY
## WCHRT_B 0.455 0.022 20.560 0.000
##
## WWCHRT_1 BY
## WCHRT_1 0.558 0.026 21.439 0.000
##
## WWCHRT_2 BY
## WCHRT_2 0.604 0.016 37.548 0.000
##
## WWCHRT_3 BY
## WCHRT_3 0.659 0.028 23.386 0.000
##
## WWCHRT_1 ON
## WWCHRT_B -0.086 0.053 -1.636 0.102
##
## WWCHRT_2 ON
## WWCHRT_1 0.178 0.032 5.499 0.000
##
## WWCHRT_3 ON
## WWCHRT_2 0.428 0.060 7.117 0.000
##
## RI_WCHRT WITH
## WWCHRT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## WCHRT_B 0.000 0.010 0.038 0.969
## WCHRT_1 0.007 0.010 0.701 0.483
## WCHRT_2 0.020 0.010 1.970 0.049
## WCHRT_3 -0.085 0.018 -4.753 0.000
##
## Variances
## RI_WCHRT 1.000 0.000 999.000 999.000
## WWCHRT_B 1.000 0.000 999.000 999.000
##
## Residual Variances
## WCHRT_B 0.000 999.000 999.000 999.000
## WCHRT_1 0.000 999.000 999.000 999.000
## WCHRT_2 0.000 999.000 999.000 999.000
## WCHRT_3 0.000 999.000 999.000 999.000
## WWCHRT_1 0.993 0.009 108.559 0.000
## WWCHRT_2 0.968 0.011 84.398 0.000
## WWCHRT_3 0.817 0.051 15.893 0.000
##
##
## R-SQUARE
##
## Observed Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WCHRT_B 1.000 999.000 999.000 999.000
## WCHRT_1 1.000 999.000 999.000 999.000
## WCHRT_2 1.000 999.000 999.000 999.000
## WCHRT_3 1.000 999.000 999.000 999.000
##
## Latent Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WWCHRT_1 0.007 0.009 0.818 0.413
## WWCHRT_2 0.032 0.011 2.749 0.006
## WWCHRT_3 0.183 0.051 3.558 0.000
##
##
## MODEL MODIFICATION INDICES
##
## NOTE: Modification indices for direct effects of observed dependent variables
## regressed on covariates may not be included. To include these, request
## MODINDICES (ALL).
##
## Minimum M.I. value for printing the modification index 10.000
##
## M.I. E.P.C. Std E.P.C. StdYX E.P.C.
##
## No modification indices above the minimum value.
##
##
##
## DIAGRAM INFORMATION
##
## Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram.
## If running Mplus from the Mplus Diagrammer, the diagram opens automatically.
##
## Diagram output
## c:\users\ocrobert\onedrive - indiana university\abcd\obesity week\mplus\wchr unconstrained.dgm
##
## Beginning Time: 12:36:16
## Ending Time: 12:36:16
## Elapsed Time: 00:00:00
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2023 Muthen & Muthen
# Specify the path to your Mplus output file
output_path <- "C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Obesity Week/Mplus/wchr constrained.out"
# Read and print the file
output_content <- readLines(output_path)
cat(output_content, sep = "\n")
## Mplus VERSION 8.10
## MUTHEN & MUTHEN
## 07/01/2024 12:39 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Waist Circumference to Height Ratio Unconstrained Model
## DATA: FILE = "Fullsample_race3_sex_subset.dat";
## VARIABLE:
## NAMES = sex race3 age_B int_B intt_B age_1 int_1 intt_1
## age_2 int_2 intt_2 age_3 int_3 intt_3 wchr_B wchrt_B
## wchr_1 wchrt_1 wchr_2 wchrt_2 wchr_3 wchrt_3 pub_B pubt_B
## pub_1 pubt_1 pub_2 pubt_2 pub_3 pubt_3 int_p_ave famc_ave bw_lbs matage
## matalc_ave matcig_ave matmar_ave ses_lt;
## MISSING=.;
##
## USEVARIABLES ARE
## wchrt_b wchrt_1
## wchrt_2 wchrt_3;
##
## ANALYSIS:
## ESTIMATOR=MLR;
## MODEL=NOCOVARIANCES;
##
## MODEL:
## ! Estimate random wchrtercept)
## RI_wchrt BY wchrt_b@1 wchrt_1@1 wchrt_2@1 wchrt_3@1;
##
## ! Create within-person centered variables
## wwchrt_b BY wchrt_b@1;
## wwchrt_1 BY wchrt_1@1;
## wwchrt_2 BY wchrt_2@1;
## wwchrt_3 BY wchrt_3@1;
##
## ! Estimate the Lagged Effects
## wwchrt_1 ON wwchrt_b (1);
## wwchrt_2 ON wwchrt_1 (1);
## wwchrt_3 ON wwchrt_2 (1);
##
## ! Constrain the measurement error variances to zero
## wchrt_b@0;
## wchrt_1@0;
## wchrt_2@0;
## wchrt_3@0;
##
## ! ask for variances for all variables that are included;
## [wchrt_b wchrt_1 wchrt_2 wchrt_3];
##
## ! Fix the correlation between the individual factors and the other
## ! exogenous variables to zero (by default these would be estimated)
## RI_wchrt with wwchrt_b@0;
##
## OUTPUT: STDYX MODINDICES;
##
##
##
## *** WARNING
## Data set contains cases with missing on all variables.
## These cases were not included in the analysis.
## Number of cases with missing on all variables: 2095
## 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
##
##
##
## Waist Circumference to Height Ratio Unconstrained Model
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 10361
##
## Number of dependent variables 4
## Number of independent variables 0
## Number of continuous latent variables 5
##
## Observed dependent variables
##
## Continuous
## WCHRT_B WCHRT_1 WCHRT_2 WCHRT_3
##
## Continuous latent variables
## RI_WCHRT WWCHRT_B WWCHRT_1 WWCHRT_2 WWCHRT_3
##
##
## Estimator MLR
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Fullsample_race3_sex_subset.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 13
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## WCHRT_B WCHRT_1 WCHRT_2 WCHRT_3
## ________ ________ ________ ________
## WCHRT_B 0.998
## WCHRT_1 0.936 0.937
## WCHRT_2 0.763 0.746 0.764
## WCHRT_3 0.169 0.166 0.153 0.169
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## WCHRT_B 0.000 1.308 -0.241 0.01% -0.055 -0.029 -0.014
## 10342.000 0.005 4.917 0.891 0.01% 0.002 0.051
## WCHRT_1 0.000 2.054 -0.213 0.01% -0.058 -0.031 -0.016
## 9711.000 0.006 13.985 0.997 0.01% 0.001 0.054
## WCHRT_2 0.000 1.516 -0.220 0.01% -0.062 -0.033 -0.017
## 7917.000 0.006 6.773 0.922 0.01% 0.002 0.056
## WCHRT_3 0.000 1.723 -0.149 0.06% -0.068 -0.038 -0.022
## 1754.000 0.007 7.313 0.770 0.06% -0.001 0.064
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 10
##
## Loglikelihood
##
## H0 Value 42844.638
## H0 Scaling Correction Factor 9.5102
## for MLR
## H1 Value 42910.217
## H1 Scaling Correction Factor 7.7273
## for MLR
##
## Information Criteria
##
## Akaike (AIC) -85669.277
## Bayesian (BIC) -85596.819
## Sample-Size Adjusted BIC -85628.597
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 40.107*
## Degrees of Freedom 4
## P-Value 0.0000
## Scaling Correction Factor 3.2702
## for MLR
##
## * The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
## for chi-square difference testing in the regular way. MLM, MLR and WLSM
## chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
## and ULSMV difference testing is done using the DIFFTEST option.
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.030
## 90 Percent C.I. 0.022 0.038
## Probability RMSEA <= .05 1.000
##
## CFI/TLI
##
## CFI 0.968
## TLI 0.952
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 1123.303
## Degrees of Freedom 6
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.039
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_WCHRT BY
## WCHRT_B 1.000 0.000 999.000 999.000
## WCHRT_1 1.000 0.000 999.000 999.000
## WCHRT_2 1.000 0.000 999.000 999.000
## WCHRT_3 1.000 0.000 999.000 999.000
##
## WWCHRT_B BY
## WCHRT_B 1.000 0.000 999.000 999.000
##
## WWCHRT_1 BY
## WCHRT_1 1.000 0.000 999.000 999.000
##
## WWCHRT_2 BY
## WCHRT_2 1.000 0.000 999.000 999.000
##
## WWCHRT_3 BY
## WCHRT_3 1.000 0.000 999.000 999.000
##
## WWCHRT_1 ON
## WWCHRT_B 0.260 0.041 6.405 0.000
##
## WWCHRT_2 ON
## WWCHRT_1 0.260 0.041 6.405 0.000
##
## WWCHRT_3 ON
## WWCHRT_2 0.260 0.041 6.405 0.000
##
## RI_WCHRT WITH
## WWCHRT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## WCHRT_B 0.000 0.001 0.041 0.967
## WCHRT_1 0.001 0.001 0.731 0.465
## WCHRT_2 0.001 0.001 1.772 0.076
## WCHRT_3 -0.006 0.001 -4.587 0.000
##
## Variances
## RI_WCHRT 0.004 0.000 37.613 0.000
## WWCHRT_B 0.001 0.000 9.894 0.000
##
## Residual Variances
## WCHRT_B 0.000 0.000 999.000 999.000
## WCHRT_1 0.000 0.000 999.000 999.000
## WCHRT_2 0.000 0.000 999.000 999.000
## WCHRT_3 0.000 0.000 999.000 999.000
## WWCHRT_1 0.002 0.000 10.401 0.000
## WWCHRT_2 0.002 0.000 11.570 0.000
## WWCHRT_3 0.002 0.000 5.880 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.295E-05
## (ratio of smallest to largest eigenvalue)
##
##
## STANDARDIZED MODEL RESULTS
##
##
## STDYX Standardization
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_WCHRT BY
## WCHRT_B 0.854 0.013 66.688 0.000
## WCHRT_1 0.783 0.014 54.523 0.000
## WCHRT_2 0.788 0.015 51.931 0.000
## WCHRT_3 0.770 0.026 29.339 0.000
##
## WWCHRT_B BY
## WCHRT_B 0.520 0.021 24.683 0.000
##
## WWCHRT_1 BY
## WCHRT_1 0.622 0.018 34.467 0.000
##
## WWCHRT_2 BY
## WCHRT_2 0.615 0.019 31.647 0.000
##
## WWCHRT_3 BY
## WCHRT_3 0.638 0.032 20.151 0.000
##
## WWCHRT_1 ON
## WWCHRT_B 0.199 0.034 5.853 0.000
##
## WWCHRT_2 ON
## WWCHRT_1 0.265 0.039 6.821 0.000
##
## WWCHRT_3 ON
## WWCHRT_2 0.245 0.045 5.440 0.000
##
## RI_WCHRT WITH
## WWCHRT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## WCHRT_B 0.000 0.010 0.041 0.967
## WCHRT_1 0.007 0.010 0.736 0.462
## WCHRT_2 0.019 0.011 1.797 0.072
## WCHRT_3 -0.082 0.019 -4.235 0.000
##
## Variances
## RI_WCHRT 1.000 0.000 999.000 999.000
## WWCHRT_B 1.000 0.000 999.000 999.000
##
## Residual Variances
## WCHRT_B 0.000 999.000 999.000 999.000
## WCHRT_1 0.000 999.000 999.000 999.000
## WCHRT_2 0.000 999.000 999.000 999.000
## WCHRT_3 0.000 999.000 999.000 999.000
## WWCHRT_1 0.960 0.014 71.035 0.000
## WWCHRT_2 0.930 0.021 45.244 0.000
## WWCHRT_3 0.940 0.022 42.644 0.000
##
##
## R-SQUARE
##
## Observed Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WCHRT_B 1.000 999.000 999.000 999.000
## WCHRT_1 1.000 999.000 999.000 999.000
## WCHRT_2 1.000 999.000 999.000 999.000
## WCHRT_3 1.000 999.000 999.000 999.000
##
## Latent Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WWCHRT_1 0.040 0.014 2.926 0.003
## WWCHRT_2 0.070 0.021 3.411 0.001
## WWCHRT_3 0.060 0.022 2.720 0.007
##
##
## MODEL MODIFICATION INDICES
##
## NOTE: Modification indices for direct effects of observed dependent variables
## regressed on covariates may not be included. To include these, request
## MODINDICES (ALL).
##
## Minimum M.I. value for printing the modification index 10.000
##
## M.I. E.P.C. Std E.P.C. StdYX E.P.C.
##
## BY Statements
##
## RI_WCHRT BY WCHRT_B 10.309 -0.153 -0.009 -0.131
## RI_WCHRT BY WCHRT_1 12.536 -0.078 -0.005 -0.061
## RI_WCHRT BY WCHRT_2 14.382 0.080 0.005 0.063
## WWCHRT_B BY WCHRT_B 12.868 0.756 0.028 0.393
## WWCHRT_B BY WCHRT_1 15.096 -0.190 -0.007 -0.091
## WWCHRT_B BY WCHRT_2 20.040 0.322 0.012 0.154
## WWCHRT_B BY WCHRT_3 10.618 -0.299 -0.011 -0.140
## WWCHRT_1 BY WCHRT_B 12.868 -0.125 -0.006 -0.085
## WWCHRT_1 BY WCHRT_3 11.005 0.194 0.009 0.119
## WWCHRT_2 BY WCHRT_B 11.141 0.195 0.009 0.130
## WWCHRT_2 BY WCHRT_3 16.664 0.265 0.012 0.159
## WWCHRT_3 BY WCHRT_B 11.836 -0.181 -0.009 -0.128
## WWCHRT_3 BY WCHRT_2 10.472 0.206 0.010 0.135
## WWCHRT_3 BY WCHRT_3 16.665 1.020 0.051 0.651
##
## ON/BY Statements
##
## RI_WCHRT ON WWCHRT_B /
## WWCHRT_B BY RI_WCHRT 21.358 -0.602 -0.366 -0.366
## RI_WCHRT ON WWCHRT_1 /
## WWCHRT_1 BY RI_WCHRT 12.098 -0.159 -0.126 -0.126
## RI_WCHRT ON WWCHRT_2 /
## WWCHRT_2 BY RI_WCHRT 16.659 0.186 0.146 0.146
## RI_WCHRT ON WWCHRT_3 /
## WWCHRT_3 BY RI_WCHRT 12.556 0.234 0.194 0.194
## WWCHRT_B ON RI_WCHRT /
## RI_WCHRT BY WWCHRT_B 21.358 -0.223 -0.366 -0.366
## WWCHRT_B ON WWCHRT_1 /
## WWCHRT_1 BY WWCHRT_B 12.868 -0.120 -0.157 -0.157
## WWCHRT_B ON WWCHRT_2 /
## WWCHRT_2 BY WWCHRT_B 12.251 0.230 0.295 0.295
## WWCHRT_1 ON WWCHRT_B /
## WWCHRT_B BY WWCHRT_1 12.868 -0.191 -0.146 -0.146
## WWCHRT_1 ON WWCHRT_1 /
## WWCHRT_1 BY WWCHRT_1 12.868 -0.756 -0.756 -0.756
## WWCHRT_1 ON WWCHRT_3 /
## WWCHRT_3 BY WWCHRT_1 10.520 0.159 0.165 0.165
## WWCHRT_2 ON RI_WCHRT /
## RI_WCHRT BY WWCHRT_2 17.274 0.090 0.115 0.115
## WWCHRT_2 ON WWCHRT_B /
## WWCHRT_B BY WWCHRT_2 18.626 0.351 0.274 0.274
## WWCHRT_2 ON WWCHRT_3 /
## WWCHRT_3 BY WWCHRT_2 10.879 0.198 0.210 0.210
## WWCHRT_3 ON WWCHRT_B /
## WWCHRT_B BY WWCHRT_3 10.618 -0.299 -0.219 -0.219
## WWCHRT_3 ON WWCHRT_1 /
## WWCHRT_1 BY WWCHRT_3 11.005 0.194 0.186 0.186
## WWCHRT_3 ON WWCHRT_2 /
## WWCHRT_2 BY WWCHRT_3 16.664 0.212 0.199 0.199
## WWCHRT_3 ON WWCHRT_3 /
## WWCHRT_3 BY WWCHRT_3 16.665 1.020 1.020 1.020
##
## WITH Statements
##
## WCHRT_1 WITH WCHRT_B 16.379 0.000 0.000 999.000
## WCHRT_2 WITH WCHRT_B 21.686 0.000 0.000 999.000
## WCHRT_3 WITH WCHRT_B 13.418 0.000 0.000 999.000
## WWCHRT_B WITH RI_WCHRT 21.358 -0.001 -0.366 -0.366
## WWCHRT_1 WITH WWCHRT_B 12.868 0.000 -0.153 -0.153
## WWCHRT_2 WITH RI_WCHRT 17.274 0.000 0.119 0.119
## WWCHRT_2 WITH WWCHRT_B 18.626 0.000 0.284 0.284
## WWCHRT_3 WITH WWCHRT_B 10.618 0.000 -0.226 -0.226
## WWCHRT_3 WITH WWCHRT_1 13.024 0.000 0.192 0.192
##
## Variances/Residual Variances
##
## WCHRT_B 12.868 0.001 0.001 0.204
##
##
## DIAGRAM INFORMATION
##
## Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram.
## If running Mplus from the Mplus Diagrammer, the diagram opens automatically.
##
## Diagram output
## c:\users\ocrobert\onedrive - indiana university\abcd\obesity week\mplus\wchr constrained.dgm
##
## Beginning Time: 12:39:37
## Ending Time: 12:39:38
## Elapsed Time: 00:00:01
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2023 Muthen & Muthen
https://www.statmodel.com/chidiff.shtml
#install.packages("stats")
library(stats)
# Number of Free Parameters 12
# Loglikelihood
# H0 Value 42900.924
# H0 Scaling Correction Factor 8.5361
# for MLR
# H1 Value 42910.217
# H1 Scaling Correction Factor 7.7273
# for MLR
# Unconstrained
L1 <- 42900.924 # Loglikelihood for the H1 model
c1 <- 8.5361 # Scaling correction factor for the H1 model
p1 <- 12 # Number of parameters for the H1 model
# Number of Free Parameters 10
# Loglikelihood
# H0 Value 42844.638
# H0 Scaling Correction Factor 9.5102
# for MLR
# H1 Value 42910.217
# H1 Scaling Correction Factor 7.7273
# for MLR
# Constrained
L0 <- 42844.638 # Loglikelihood for the H0 model
c0 <- 9.5102 # Scaling correction factor for the H0 model
p0 <- 10 # Number of parameters for the H0 model
# Compute the difference test scaling correction (cd)
cd <- (p0 * c0 - p1 * c1) / (p0 - p1)
# Compute the chi-square difference test (TRd)
TRd <- -2 * (L0 - L1) / cd
# Define degrees of freedom
df <- p1 - p0
# Calculate p-value
p_value <- pchisq(TRd, df = df, lower.tail = FALSE)
# Print results
cat("Difference Test Scaling Correction (cd):", cd, "\n")
## Difference Test Scaling Correction (cd): 3.6656
cat("Chi-Square Difference Test (TRd):", TRd, "\n")
## Chi-Square Difference Test (TRd): 30.71039
cat("p-value:", p_value, "\n")
## p-value: 2.144489e-07
# Print "Cannot constrain" if p-value is less than 0.05
if (p_value < 0.05) {
cat("Cannot constrain\n")
} else {
cat("Constraining is acceptable\n")
}
## Cannot constrain
# Specify the path to your Mplus output file
output_path <- "C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Obesity Week/Mplus/puberty unconstrained.out"
# Read and print the file
output_content <- readLines(output_path)
cat(output_content, sep = "\n")
## Mplus VERSION 8.10
## MUTHEN & MUTHEN
## 07/01/2024 1:02 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Puberty Unconstrained Model
## DATA: FILE = "Fullsample_race3_sex_subset.dat";
## VARIABLE:
## NAMES = sex race3 age_B int_B intt_B age_1 int_1 intt_1
## age_2 int_2 intt_2 age_3 int_3 intt_3 wchr_B wchrt_B
## wchr_1 wchrt_1 wchr_2 wchrt_2 wchr_3 wchrt_3 pub_B pubt_B
## pub_1 pubt_1 pub_2 pubt_2 pub_3 pubt_3 int_p_ave famc_ave bw_lbs matage
## matalc_ave matcig_ave matmar_ave ses_lt;
## MISSING=.;
##
## USEVARIABLES ARE
## pubt_b pubt_1
## pubt_2 pubt_3;
##
## ANALYSIS:
## ESTIMATOR=MLR;
## MODEL=NOCOVARIANCES;
##
## MODEL:
## ! Estimate random pubtercept)
## RI_pubt BY pubt_b@1 pubt_1@1 pubt_2@1 pubt_3@1;
##
## ! Create within-person centered variables
## wpubt_b BY pubt_b@1;
## wpubt_1 BY pubt_1@1;
## wpubt_2 BY pubt_2@1;
## wpubt_3 BY pubt_3@1;
##
## ! Estimate the Lagged Effects
## wpubt_1 ON wpubt_b;
## wpubt_2 ON wpubt_1;
## wpubt_3 ON wpubt_2;
##
## ! Constrain the measurement error variances to zero
## pubt_b@0;
## pubt_1@0;
## pubt_2@0;
## pubt_3@0;
##
## ! ask for variances for all variables that are included;
## [pubt_b pubt_1 pubt_2 pubt_3];
##
## ! Fix the correlation between the individual factors and the other
## ! exogenous variables to zero (by default these would be estimated)
## RI_pubt with wpubt_b@0;
##
## OUTPUT: STDYX MODINDICES;
##
##
##
## *** WARNING
## Data set contains cases with missing on all variables.
## These cases were not included in the analysis.
## Number of cases with missing on all variables: 2299
## 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
##
##
##
## Puberty Unconstrained Model
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 10157
##
## Number of dependent variables 4
## Number of independent variables 0
## Number of continuous latent variables 5
##
## Observed dependent variables
##
## Continuous
## PUBT_B PUBT_1 PUBT_2 PUBT_3
##
## Continuous latent variables
## RI_PUBT WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
##
##
## Estimator MLR
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Fullsample_race3_sex_subset.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 15
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## PUBT_B PUBT_1 PUBT_2 PUBT_3
## ________ ________ ________ ________
## PUBT_B 0.823
## PUBT_1 0.375 0.416
## PUBT_2 0.728 0.393 0.892
## PUBT_3 0.691 0.387 0.800 0.846
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## PUBT_B 0.000 0.125 -1.606 0.08% -0.935 0.035 0.053
## 8363.000 0.652 -0.580 3.081 0.01% 0.073 0.820
## PUBT_1 0.000 -0.042 -2.124 0.05% -0.843 -0.054 0.036
## 4224.000 0.636 -0.382 3.082 0.02% 0.157 0.790
## PUBT_2 0.000 -0.276 -2.839 0.01% -0.610 -0.151 0.041
## 9064.000 0.600 0.112 3.016 0.01% 0.232 0.660
## PUBT_3 0.000 -0.457 -2.940 0.01% -0.583 -0.036 0.115
## 8592.000 0.552 0.549 2.534 0.02% 0.252 0.554
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 12
##
## Loglikelihood
##
## H0 Value -32956.741
## H0 Scaling Correction Factor 1.0634
## for MLR
## H1 Value -32935.686
## H1 Scaling Correction Factor 1.0532
## for MLR
##
## Information Criteria
##
## Akaike (AIC) 65937.483
## Bayesian (BIC) 66024.194
## Sample-Size Adjusted BIC 65986.059
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 42.454*
## Degrees of Freedom 2
## P-Value 0.0000
## Scaling Correction Factor 0.9919
## for MLR
##
## * The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
## for chi-square difference testing in the regular way. MLM, MLR and WLSM
## chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
## and ULSMV difference testing is done using the DIFFTEST option.
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.045
## 90 Percent C.I. 0.034 0.057
## Probability RMSEA <= .05 0.753
##
## CFI/TLI
##
## CFI 0.990
## TLI 0.970
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 4108.968
## Degrees of Freedom 6
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.016
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_PUBT BY
## PUBT_B 1.000 0.000 999.000 999.000
## PUBT_1 1.000 0.000 999.000 999.000
## PUBT_2 1.000 0.000 999.000 999.000
## PUBT_3 1.000 0.000 999.000 999.000
##
## WPUBT_B BY
## PUBT_B 1.000 0.000 999.000 999.000
##
## WPUBT_1 BY
## PUBT_1 1.000 0.000 999.000 999.000
##
## WPUBT_2 BY
## PUBT_2 1.000 0.000 999.000 999.000
##
## WPUBT_3 BY
## PUBT_3 1.000 0.000 999.000 999.000
##
## WPUBT_1 ON
## WPUBT_B 0.250 0.022 11.203 0.000
##
## WPUBT_2 ON
## WPUBT_1 0.369 0.020 18.464 0.000
##
## WPUBT_3 ON
## WPUBT_2 0.385 0.016 24.451 0.000
##
## RI_PUBT WITH
## WPUBT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## PUBT_B 0.000 0.009 0.051 0.959
## PUBT_1 0.029 0.011 2.604 0.009
## PUBT_2 0.004 0.008 0.525 0.600
## PUBT_3 0.003 0.008 0.399 0.690
##
## Variances
## RI_PUBT 0.112 0.009 12.188 0.000
## WPUBT_B 0.544 0.011 47.326 0.000
##
## Residual Variances
## PUBT_B 0.000 0.000 999.000 999.000
## PUBT_1 0.000 0.000 999.000 999.000
## PUBT_2 0.000 0.000 999.000 999.000
## PUBT_3 0.000 0.000 999.000 999.000
## WPUBT_1 0.482 0.012 38.830 0.000
## WPUBT_2 0.416 0.008 49.789 0.000
## WPUBT_3 0.369 0.008 47.181 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.277E-01
## (ratio of smallest to largest eigenvalue)
##
##
## STANDARDIZED MODEL RESULTS
##
##
## STDYX Standardization
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_PUBT BY
## PUBT_B 0.413 0.017 24.726 0.000
## PUBT_1 0.422 0.018 23.189 0.000
## PUBT_2 0.433 0.018 24.164 0.000
## PUBT_3 0.450 0.018 24.737 0.000
##
## WPUBT_B BY
## PUBT_B 0.911 0.008 119.907 0.000
##
## WPUBT_1 BY
## PUBT_1 0.906 0.008 106.773 0.000
##
## WPUBT_2 BY
## PUBT_2 0.901 0.009 104.806 0.000
##
## WPUBT_3 BY
## PUBT_3 0.893 0.009 97.417 0.000
##
## WPUBT_1 ON
## WPUBT_B 0.257 0.022 11.742 0.000
##
## WPUBT_2 ON
## WPUBT_1 0.380 0.020 18.735 0.000
##
## WPUBT_3 ON
## WPUBT_2 0.404 0.016 25.712 0.000
##
## RI_PUBT WITH
## WPUBT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## PUBT_B 0.001 0.011 0.051 0.959
## PUBT_1 0.037 0.014 2.604 0.009
## PUBT_2 0.005 0.010 0.524 0.600
## PUBT_3 0.004 0.011 0.398 0.690
##
## Variances
## RI_PUBT 1.000 0.000 999.000 999.000
## WPUBT_B 1.000 0.000 999.000 999.000
##
## Residual Variances
## PUBT_B 0.000 999.000 999.000 999.000
## PUBT_1 0.000 999.000 999.000 999.000
## PUBT_2 0.000 999.000 999.000 999.000
## PUBT_3 0.000 999.000 999.000 999.000
## WPUBT_1 0.934 0.011 83.207 0.000
## WPUBT_2 0.855 0.015 55.399 0.000
## WPUBT_3 0.836 0.013 65.731 0.000
##
##
## R-SQUARE
##
## Observed Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## PUBT_B 1.000 999.000 999.000 999.000
## PUBT_1 1.000 999.000 999.000 999.000
## PUBT_2 1.000 999.000 999.000 999.000
## PUBT_3 1.000 999.000 999.000 999.000
##
## Latent Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WPUBT_1 0.066 0.011 5.871 0.000
## WPUBT_2 0.145 0.015 9.368 0.000
## WPUBT_3 0.164 0.013 12.856 0.000
##
##
## MODEL MODIFICATION INDICES
##
## NOTE: Modification indices for direct effects of observed dependent variables
## regressed on covariates may not be included. To include these, request
## MODINDICES (ALL).
##
## Minimum M.I. value for printing the modification index 10.000
##
## M.I. E.P.C. Std E.P.C. StdYX E.P.C.
##
## BY Statements
##
## RI_PUBT BY PUBT_B 22.181 -0.672 -0.225 -0.278
## RI_PUBT BY PUBT_1 10.205 0.259 0.087 0.109
## WPUBT_B BY PUBT_2 10.875 0.038 0.028 0.037
## WPUBT_B BY PUBT_3 28.350 -0.085 -0.062 -0.084
## WPUBT_1 BY PUBT_2 22.178 -0.225 -0.162 -0.209
## WPUBT_1 BY PUBT_3 22.182 0.087 0.062 0.084
## WPUBT_3 BY PUBT_B 42.339 -0.174 -0.116 -0.143
## WPUBT_3 BY PUBT_1 27.548 0.099 0.066 0.083
## WPUBT_3 BY PUBT_2 22.223 -0.265 -0.176 -0.227
##
## ON/BY Statements
##
## RI_PUBT ON WPUBT_B /
## WPUBT_B BY RI_PUBT 22.166 -0.205 -0.452 -0.452
## RI_PUBT ON WPUBT_1 /
## WPUBT_1 BY RI_PUBT 22.221 0.141 0.303 0.303
## WPUBT_B ON RI_PUBT /
## RI_PUBT BY WPUBT_B 22.166 -0.996 -0.452 -0.452
## WPUBT_B ON WPUBT_3 /
## WPUBT_3 BY WPUBT_B 39.985 -0.220 -0.198 -0.198
## WPUBT_1 ON RI_PUBT /
## RI_PUBT BY WPUBT_1 22.213 0.518 0.241 0.241
## WPUBT_1 ON WPUBT_3 /
## WPUBT_3 BY WPUBT_1 28.271 0.114 0.106 0.106
## WPUBT_2 ON WPUBT_3 /
## WPUBT_3 BY WPUBT_2 22.234 -0.265 -0.252 -0.252
## WPUBT_3 ON WPUBT_B /
## WPUBT_B BY WPUBT_3 28.350 -0.085 -0.094 -0.094
## WPUBT_3 ON WPUBT_1 /
## WPUBT_1 BY WPUBT_3 22.182 0.087 0.094 0.094
##
## WITH Statements
##
## PUBT_2 WITH PUBT_B 13.292 0.022 0.022 999.000
## PUBT_2 WITH PUBT_1 42.177 -0.110 -0.110 999.000
## PUBT_3 WITH PUBT_B 35.872 -0.045 -0.045 999.000
## PUBT_3 WITH PUBT_1 31.452 0.040 0.040 999.000
## PUBT_3 WITH PUBT_2 22.233 -0.098 -0.098 999.000
## WPUBT_B WITH RI_PUBT 22.166 -0.112 -0.452 -0.452
## WPUBT_1 WITH RI_PUBT 22.213 0.058 0.250 0.250
## WPUBT_3 WITH WPUBT_B 28.350 -0.046 -0.103 -0.103
## WPUBT_3 WITH WPUBT_1 32.637 0.046 0.110 0.110
## WPUBT_3 WITH WPUBT_2 22.236 -0.098 -0.249 -0.249
##
## Variances/Residual Variances
##
## PUBT_2 22.236 0.254 0.254 0.424
##
##
## DIAGRAM INFORMATION
##
## Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram.
## If running Mplus from the Mplus Diagrammer, the diagram opens automatically.
##
## Diagram output
## c:\users\ocrobert\onedrive - indiana university\abcd\obesity week\mplus\puberty unconstrained.dgm
##
## Beginning Time: 13:02:48
## Ending Time: 13:02:49
## Elapsed Time: 00:00:01
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2023 Muthen & Muthen
# Specify the path to your Mplus output file
output_path <- "C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Obesity Week/Mplus/puberty constrained.out"
# Read and print the file
output_content <- readLines(output_path)
cat(output_content, sep = "\n")
## Mplus VERSION 8.10
## MUTHEN & MUTHEN
## 07/01/2024 1:06 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Puberty Unconstrained Model
## DATA: FILE = "Fullsample_race3_sex_subset.dat";
## VARIABLE:
## NAMES = sex race3 age_B int_B intt_B age_1 int_1 intt_1
## age_2 int_2 intt_2 age_3 int_3 intt_3 wchr_B wchrt_B
## wchr_1 wchrt_1 wchr_2 wchrt_2 wchr_3 wchrt_3 pub_B pubt_B
## pub_1 pubt_1 pub_2 pubt_2 pub_3 pubt_3 int_p_ave famc_ave bw_lbs matage
## matalc_ave matcig_ave matmar_ave ses_lt;
## MISSING=.;
##
## USEVARIABLES ARE
## pubt_b pubt_1
## pubt_2 pubt_3;
##
## ANALYSIS:
## ESTIMATOR=MLR;
## MODEL=NOCOVARIANCES;
##
## MODEL:
## ! Estimate random pubtercept)
## RI_pubt BY pubt_b@1 pubt_1@1 pubt_2@1 pubt_3@1;
##
## ! Create within-person centered variables
## wpubt_b BY pubt_b@1;
## wpubt_1 BY pubt_1@1;
## wpubt_2 BY pubt_2@1;
## wpubt_3 BY pubt_3@1;
##
## ! Estimate the Lagged Effects
## wpubt_1 ON wpubt_b (1);
## wpubt_2 ON wpubt_1 (1);
## wpubt_3 ON wpubt_2 (1);
##
## ! Constrain the measurement error variances to zero
## pubt_b@0;
## pubt_1@0;
## pubt_2@0;
## pubt_3@0;
##
## ! ask for variances for all variables that are included;
## [pubt_b pubt_1 pubt_2 pubt_3];
##
## ! Fix the correlation between the individual factors and the other
## ! exogenous variables to zero (by default these would be estimated)
## RI_pubt with wpubt_b@0;
##
## OUTPUT: STDYX MODINDICES;
##
##
##
## *** WARNING
## Data set contains cases with missing on all variables.
## These cases were not included in the analysis.
## Number of cases with missing on all variables: 2299
## 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
##
##
##
## Puberty Unconstrained Model
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 10157
##
## Number of dependent variables 4
## Number of independent variables 0
## Number of continuous latent variables 5
##
## Observed dependent variables
##
## Continuous
## PUBT_B PUBT_1 PUBT_2 PUBT_3
##
## Continuous latent variables
## RI_PUBT WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
##
##
## Estimator MLR
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Fullsample_race3_sex_subset.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 15
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## PUBT_B PUBT_1 PUBT_2 PUBT_3
## ________ ________ ________ ________
## PUBT_B 0.823
## PUBT_1 0.375 0.416
## PUBT_2 0.728 0.393 0.892
## PUBT_3 0.691 0.387 0.800 0.846
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## PUBT_B 0.000 0.125 -1.606 0.08% -0.935 0.035 0.053
## 8363.000 0.652 -0.580 3.081 0.01% 0.073 0.820
## PUBT_1 0.000 -0.042 -2.124 0.05% -0.843 -0.054 0.036
## 4224.000 0.636 -0.382 3.082 0.02% 0.157 0.790
## PUBT_2 0.000 -0.276 -2.839 0.01% -0.610 -0.151 0.041
## 9064.000 0.600 0.112 3.016 0.01% 0.232 0.660
## PUBT_3 0.000 -0.457 -2.940 0.01% -0.583 -0.036 0.115
## 8592.000 0.552 0.549 2.534 0.02% 0.252 0.554
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 10
##
## Loglikelihood
##
## H0 Value -32982.169
## H0 Scaling Correction Factor 1.0510
## for MLR
## H1 Value -32935.686
## H1 Scaling Correction Factor 1.0532
## for MLR
##
## Information Criteria
##
## Akaike (AIC) 65984.337
## Bayesian (BIC) 66056.596
## Sample-Size Adjusted BIC 66024.818
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 87.813*
## Degrees of Freedom 4
## P-Value 0.0000
## Scaling Correction Factor 1.0587
## for MLR
##
## * The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
## for chi-square difference testing in the regular way. MLM, MLR and WLSM
## chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
## and ULSMV difference testing is done using the DIFFTEST option.
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.045
## 90 Percent C.I. 0.037 0.054
## Probability RMSEA <= .05 0.806
##
## CFI/TLI
##
## CFI 0.980
## TLI 0.969
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 4108.968
## Degrees of Freedom 6
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.034
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_PUBT BY
## PUBT_B 1.000 0.000 999.000 999.000
## PUBT_1 1.000 0.000 999.000 999.000
## PUBT_2 1.000 0.000 999.000 999.000
## PUBT_3 1.000 0.000 999.000 999.000
##
## WPUBT_B BY
## PUBT_B 1.000 0.000 999.000 999.000
##
## WPUBT_1 BY
## PUBT_1 1.000 0.000 999.000 999.000
##
## WPUBT_2 BY
## PUBT_2 1.000 0.000 999.000 999.000
##
## WPUBT_3 BY
## PUBT_3 1.000 0.000 999.000 999.000
##
## WPUBT_1 ON
## WPUBT_B 0.357 0.016 22.619 0.000
##
## WPUBT_2 ON
## WPUBT_1 0.357 0.016 22.619 0.000
##
## WPUBT_3 ON
## WPUBT_2 0.357 0.016 22.619 0.000
##
## RI_PUBT WITH
## WPUBT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## PUBT_B 0.001 0.009 0.059 0.953
## PUBT_1 0.030 0.011 2.670 0.008
## PUBT_2 0.004 0.008 0.527 0.598
## PUBT_3 0.003 0.008 0.376 0.707
##
## Variances
## RI_PUBT 0.106 0.010 10.732 0.000
## WPUBT_B 0.555 0.011 50.066 0.000
##
## Residual Variances
## PUBT_B 0.000 0.000 999.000 999.000
## PUBT_1 0.000 0.000 999.000 999.000
## PUBT_2 0.000 0.000 999.000 999.000
## PUBT_3 0.000 0.000 999.000 999.000
## WPUBT_1 0.495 0.013 38.361 0.000
## WPUBT_2 0.418 0.009 48.866 0.000
## WPUBT_3 0.368 0.008 45.369 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.516E-01
## (ratio of smallest to largest eigenvalue)
##
##
## STANDARDIZED MODEL RESULTS
##
##
## STDYX Standardization
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_PUBT BY
## PUBT_B 0.401 0.018 22.266 0.000
## PUBT_1 0.398 0.020 20.253 0.000
## PUBT_2 0.423 0.020 21.005 0.000
## PUBT_3 0.445 0.021 21.446 0.000
##
## WPUBT_B BY
## PUBT_B 0.916 0.008 116.159 0.000
##
## WPUBT_1 BY
## PUBT_1 0.917 0.009 107.662 0.000
##
## WPUBT_2 BY
## PUBT_2 0.906 0.009 96.621 0.000
##
## WPUBT_3 BY
## PUBT_3 0.895 0.010 86.773 0.000
##
## WPUBT_1 ON
## WPUBT_B 0.354 0.014 25.226 0.000
##
## WPUBT_2 ON
## WPUBT_1 0.384 0.018 21.570 0.000
##
## WPUBT_3 ON
## WPUBT_2 0.380 0.017 22.237 0.000
##
## RI_PUBT WITH
## WPUBT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## PUBT_B 0.001 0.011 0.059 0.953
## PUBT_1 0.036 0.014 2.670 0.008
## PUBT_2 0.005 0.010 0.527 0.598
## PUBT_3 0.004 0.011 0.376 0.707
##
## Variances
## RI_PUBT 1.000 0.000 999.000 999.000
## WPUBT_B 1.000 0.000 999.000 999.000
##
## Residual Variances
## PUBT_B 0.000 999.000 999.000 999.000
## PUBT_1 0.000 999.000 999.000 999.000
## PUBT_2 0.000 999.000 999.000 999.000
## PUBT_3 0.000 999.000 999.000 999.000
## WPUBT_1 0.875 0.010 88.322 0.000
## WPUBT_2 0.853 0.014 62.505 0.000
## WPUBT_3 0.855 0.013 65.686 0.000
##
##
## R-SQUARE
##
## Observed Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## PUBT_B 1.000 999.000 999.000 999.000
## PUBT_1 1.000 999.000 999.000 999.000
## PUBT_2 1.000 999.000 999.000 999.000
## PUBT_3 1.000 999.000 999.000 999.000
##
## Latent Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WPUBT_1 0.125 0.010 12.613 0.000
## WPUBT_2 0.147 0.014 10.785 0.000
## WPUBT_3 0.145 0.013 11.118 0.000
##
##
## MODEL MODIFICATION INDICES
##
## NOTE: Modification indices for direct effects of observed dependent variables
## regressed on covariates may not be included. To include these, request
## MODINDICES (ALL).
##
## Minimum M.I. value for printing the modification index 10.000
##
## M.I. E.P.C. Std E.P.C. StdYX E.P.C.
##
## BY Statements
##
## RI_PUBT BY PUBT_B 55.930 -0.886 -0.289 -0.355
## WPUBT_B BY PUBT_B 45.671 0.348 0.259 0.318
## WPUBT_B BY PUBT_1 47.686 -0.119 -0.089 -0.108
## WPUBT_B BY PUBT_3 15.659 -0.068 -0.051 -0.069
## WPUBT_1 BY PUBT_B 45.675 -0.162 -0.122 -0.150
## WPUBT_1 BY PUBT_1 21.859 -0.137 -0.103 -0.125
## WPUBT_1 BY PUBT_3 41.122 0.103 0.078 0.106
## WPUBT_2 BY PUBT_3 17.884 0.062 0.043 0.059
## WPUBT_3 BY PUBT_B 60.923 -0.206 -0.135 -0.166
## WPUBT_3 BY PUBT_1 32.789 0.108 0.071 0.086
## WPUBT_3 BY PUBT_3 17.834 0.173 0.114 0.155
##
## ON/BY Statements
##
## RI_PUBT ON WPUBT_B /
## WPUBT_B BY RI_PUBT 62.300 -0.192 -0.438 -0.438
## RI_PUBT ON WPUBT_2 /
## WPUBT_2 BY RI_PUBT 12.008 0.079 0.170 0.170
## RI_PUBT ON WPUBT_3 /
## WPUBT_3 BY RI_PUBT 17.836 0.083 0.167 0.167
## WPUBT_B ON RI_PUBT /
## RI_PUBT BY WPUBT_B 62.300 -1.000 -0.438 -0.438
## WPUBT_B ON WPUBT_1 /
## WPUBT_1 BY WPUBT_B 45.680 -0.139 -0.140 -0.140
## WPUBT_B ON WPUBT_3 /
## WPUBT_3 BY WPUBT_B 44.654 -0.263 -0.232 -0.232
## WPUBT_1 ON WPUBT_B /
## WPUBT_B BY WPUBT_1 45.679 -0.098 -0.097 -0.097
## WPUBT_1 ON WPUBT_1 /
## WPUBT_1 BY WPUBT_1 45.714 -0.348 -0.348 -0.348
## WPUBT_1 ON WPUBT_3 /
## WPUBT_3 BY WPUBT_1 53.790 0.152 0.132 0.132
## WPUBT_2 ON RI_PUBT /
## RI_PUBT BY WPUBT_2 13.784 0.286 0.133 0.133
## WPUBT_3 ON WPUBT_B /
## WPUBT_B BY WPUBT_3 15.659 -0.068 -0.077 -0.077
## WPUBT_3 ON WPUBT_1 /
## WPUBT_1 BY WPUBT_3 41.122 0.103 0.118 0.118
## WPUBT_3 ON WPUBT_2 /
## WPUBT_2 BY WPUBT_3 17.884 0.031 0.033 0.033
## WPUBT_3 ON WPUBT_3 /
## WPUBT_3 BY WPUBT_3 17.834 0.173 0.173 0.173
##
## WITH Statements
##
## PUBT_1 WITH PUBT_B 50.010 -0.070 -0.070 999.000
## PUBT_3 WITH PUBT_B 35.220 -0.046 -0.046 999.000
## PUBT_3 WITH PUBT_1 38.303 0.045 0.045 999.000
## WPUBT_B WITH RI_PUBT 62.300 -0.106 -0.438 -0.438
## WPUBT_1 WITH WPUBT_B 45.679 -0.069 -0.131 -0.131
## WPUBT_2 WITH RI_PUBT 13.784 0.030 0.144 0.144
## WPUBT_3 WITH WPUBT_B 15.659 -0.038 -0.084 -0.084
## WPUBT_3 WITH WPUBT_1 53.530 0.055 0.129 0.129
##
## Variances/Residual Variances
##
## PUBT_B 45.672 0.193 0.193 0.292
##
##
## DIAGRAM INFORMATION
##
## Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram.
## If running Mplus from the Mplus Diagrammer, the diagram opens automatically.
##
## Diagram output
## c:\users\ocrobert\onedrive - indiana university\abcd\obesity week\mplus\puberty constrained.dgm
##
## Beginning Time: 13:06:18
## Ending Time: 13:06:18
## Elapsed Time: 00:00:00
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2023 Muthen & Muthen
https://www.statmodel.com/chidiff.shtml
#install.packages("stats")
library(stats)
# Number of Free Parameters 12
# Loglikelihood
# H0 Value -32956.741
# H0 Scaling Correction Factor 1.0634
# for MLR
# H1 Value -32935.686
# H1 Scaling Correction Factor 1.0532
# for MLR
# Unconstrained
L1 <- -32956.741 # Loglikelihood for the H1 model
c1 <- 1.0634 # Scaling correction factor for the H1 model
p1 <- 12 # Number of parameters for the H1 model
# Number of Free Parameters 10
# Loglikelihood
# H0 Value -32982.169
# H0 Scaling Correction Factor 1.0510
# for MLR
# H1 Value -32935.686
# H1 Scaling Correction Factor 1.0532
# for MLR
# Constrained
L0 <- -32982.169 # Loglikelihood for the H0 model
c0 <- 1.0510 # Scaling correction factor for the H0 model
p0 <- 10 # Number of parameters for the H0 model
# Compute the difference test scaling correction (cd)
cd <- (p0 * c0 - p1 * c1) / (p0 - p1)
# Compute the chi-square difference test (TRd)
TRd <- -2 * (L0 - L1) / cd
# Define degrees of freedom
df <- p1 - p0
# Calculate p-value
p_value <- pchisq(TRd, df = df, lower.tail = FALSE)
# Print results
cat("Difference Test Scaling Correction (cd):", cd, "\n")
## Difference Test Scaling Correction (cd): 1.1254
cat("Chi-Square Difference Test (TRd):", TRd, "\n")
## Chi-Square Difference Test (TRd): 45.18927
cat("p-value:", p_value, "\n")
## p-value: 1.539131e-10
# Print "Cannot constrain" if p-value is less than 0.05
if (p_value < 0.05) {
cat("Cannot constrain\n")
} else {
cat("Constraining is acceptable\n")
}
## Cannot constrain
Summary: Based on the above model building steps of estimating models with unconstrained and constrained cross lagged paths and then performing likelihood ratio tests to rest if constraining results in decrements in model fit, all cross lagged paths should be left unconstrained.
Full random intercept cross lag panel model specified according to Hamaker et al.,2015 (DOI: 10.1037/a0038889)
# Specify the path to your Mplus output file
output_path <- "C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Obesity Week/Mplus/all full model.out"
# Read and print the file
output_content <- readLines(output_path)
cat(output_content, sep = "\n")
## Mplus VERSION 8.10
## MUTHEN & MUTHEN
## 07/01/2024 2:02 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Full Sample Model
## DATA: FILE = "Fullsample_race3_sex_subset.dat";
## VARIABLE:
## NAMES = sex race3 age_B int_B intt_B age_1 int_1 intt_1
## age_2 int_2 intt_2 age_3 int_3 intt_3 wchr_B wchrt_B
## wchr_1 wchrt_1 wchr_2 wchrt_2 wchr_3 wchrt_3 pub_B pubt_B
## pub_1 pubt_1 pub_2 pubt_2 pub_3 pubt_3 int_p_ave famc_ave bw_lbs matage
## matalc_ave matcig_ave matmar_ave ses_lt;
## MISSING=.;
##
## USEVARIABLES ARE
## wchrt_b wchrt_1
## wchrt_2 wchrt_3
## intt_b intt_1
## intt_2 intt_3
## pubt_b pubt_1
## pubt_2 pubt_3;
##
## DEFINE:
## wchrt_b = wchrt_b*10;
## wchrt_1 = wchrt_1*10;
## wchrt_2 = wchrt_2*10;
## wchrt_3 = wchrt_3*10;
##
## ANALYSIS:
## ESTIMATOR=MLR;
## MODEL=NOCOVARIANCES;
##
## MODEL:
## ! Estimate random inttercept)
## RI_intt BY intt_b@1 intt_1@1 intt_2@1 intt_3@1;
## RI_pubtT BY pubt_b@1 pubt_1@1 pubt_2@1 pubt_3@1;
## RI_wchrt BY wchrt_b@1 wchrt_1@1 wchrt_2@1 wchrt_3@1;
##
## ! RI correlations
## RI_intt with RI_pubtT RI_wchrt;
## RI_pubtT with RI_wchrt;
##
## ! Create within-person centered variables
## wintt_b BY intt_b@1;
## wintt_1 BY intt_1@1;
## wintt_2 BY intt_2@1;
## wintt_3 BY intt_3@1;
##
## wwchrt_b BY wchrt_b@1;
## wwchrt_1 BY wchrt_1@1;
## wwchrt_2 BY wchrt_2@1;
## wwchrt_3 BY wchrt_3@1;
##
## wpubt_b BY pubt_b@1;
## wpubt_1 BY pubt_1@1;
## wpubt_2 BY pubt_2@1;
## wpubt_3 BY pubt_3@1;
##
## ! Constrain the measurement error variances to zero
## intt_b@0;
## intt_1@0;
## intt_2@0;
## intt_3@0;
##
## wchrt_b@0;
## wchrt_1@0;
## wchrt_2@0;
## wchrt_3@0;
##
## pubt_b@0;
## pubt_1@0;
## pubt_2@0;
## pubt_3@0;
##
## ! Estimate the Lagged Effects
## wwchrt_1 ON wwchrt_b;
## wwchrt_1 ON wintt_b;
## wwchrt_1 ON wpubt_b;
##
## wwchrt_2 ON wwchrt_1;
## wwchrt_2 ON wintt_1;
## wwchrt_2 ON wpubt_1;
##
## wwchrt_3 ON wwchrt_2;
## wwchrt_3 ON wintt_2;
## wwchrt_3 ON wpubt_2;
##
## wpubt_1 ON wpubt_b;
## wpubt_1 ON wintt_b;
## wpubt_1 ON wwchrt_b;
##
## wpubt_2 ON wpubt_1;
## wpubt_2 ON wintt_1;
## wpubt_2 ON wwchrt_1;
##
## wpubt_3 ON wpubt_2;
## wpubt_3 ON wintt_2;
## wpubt_3 ON wwchrt_2;
##
## wintt_1 ON wintt_b;
## wintt_1 ON wpubt_b;
## wintt_1 ON wwchrt_b;
##
## wintt_2 ON wintt_1;
## wintt_2 ON wpubt_1;
## wintt_2 ON wwchrt_1;
##
## wintt_3 ON wintt_2;
## wintt_3 ON wpubt_2;
## wintt_3 ON wwchrt_2;
##
## ! Estimate the covariance between the within-person
## ! centered variables at the first wave
## wintt_b with wpubt_b;
## wwchrt_b with wintt_b wpubt_b;
##
## ! Estimate covariances between residuals of within-person components
## ! (i.e., innovations)
## wintt_1 with wpubt_1 wwchrt_1;
## wpubt_1 with wwchrt_1;
##
## wintt_2 with wpubt_2 wwchrt_2;
## wpubt_2 with wwchrt_2;
##
## wintt_3 with wpubt_3 wwchrt_3;
## wpubt_3 with wwchrt_3;
##
## ! ask for variances for all variables that are included;
## [intt_b intt_1 intt_2 intt_3];
## [pubt_b pubt_1 pubt_2 pubt_3];
## [wchrt_b wchrt_1 wchrt_2 wchrt_3];
##
## ! Fix the correlation between the individual factors and the other
## ! exogenous variables to zero (by default these would be estimated)
## RI_wchrt with wwchrt_b@0 wintt_b@0 wpubt_b@0;
## RI_intt with wwchrt_b@0 wintt_b@0 wpubt_b@0;
## RI_pubtT with wwchrt_b@0 wintt_b@0 wpubt_b@0;
##
## OUTPUT: STDYX MODINDICES Tech4;
##
##
##
## *** WARNING
## Data set contains cases with missing on all variables.
## These cases were not included in the analysis.
## Number of cases with missing on all variables: 2094
## 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
##
##
##
## Full Sample Model
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 10362
##
## Number of dependent variables 12
## Number of independent variables 0
## Number of continuous latent variables 15
##
## Observed dependent variables
##
## Continuous
## WCHRT_B WCHRT_1 WCHRT_2 WCHRT_3 INTT_B INTT_1
## INTT_2 INTT_3 PUBT_B PUBT_1 PUBT_2 PUBT_3
##
## Continuous latent variables
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1 WINTT_2
## WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2 WWCHRT_3 WPUBT_B
## WPUBT_1 WPUBT_2 WPUBT_3
##
##
## Estimator MLR
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Fullsample_race3_sex_subset.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 208
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## WCHRT_B WCHRT_1 WCHRT_2 WCHRT_3 INTT_B
## ________ ________ ________ ________ ________
## WCHRT_B 0.998
## WCHRT_1 0.936 0.937
## WCHRT_2 0.763 0.746 0.764
## WCHRT_3 0.169 0.166 0.153 0.169
## INTT_B 0.998 0.937 0.764 0.169 0.999
## INTT_1 0.942 0.936 0.749 0.167 0.943
## INTT_2 0.917 0.891 0.763 0.167 0.918
## INTT_3 0.850 0.827 0.709 0.168 0.851
## PUBT_B 0.806 0.757 0.626 0.149 0.807
## PUBT_1 0.407 0.405 0.392 0.102 0.407
## PUBT_2 0.873 0.849 0.727 0.158 0.874
## PUBT_3 0.828 0.805 0.691 0.162 0.829
##
##
## Covariance Coverage
## INTT_1 INTT_2 INTT_3 PUBT_B PUBT_1
## ________ ________ ________ ________ ________
## INTT_1 0.944
## INTT_2 0.897 0.919
## INTT_3 0.833 0.833 0.851
## PUBT_B 0.762 0.743 0.690 0.807
## PUBT_1 0.407 0.398 0.384 0.368 0.408
## PUBT_2 0.854 0.869 0.794 0.714 0.385
## PUBT_3 0.810 0.810 0.812 0.677 0.379
##
##
## Covariance Coverage
## PUBT_2 PUBT_3
## ________ ________
## PUBT_2 0.875
## PUBT_3 0.784 0.829
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## WCHRT_B 0.000 1.308 -2.415 0.01% -0.550 -0.290 -0.144
## 10342.000 0.487 4.917 8.909 0.01% 0.018 0.508
## WCHRT_1 0.000 2.054 -2.125 0.01% -0.582 -0.308 -0.165
## 9711.000 0.566 13.985 9.968 0.01% 0.007 0.536
## WCHRT_2 0.000 1.516 -2.197 0.01% -0.622 -0.327 -0.174
## 7917.000 0.597 6.773 9.219 0.01% 0.019 0.563
## WCHRT_3 0.000 1.723 -1.493 0.06% -0.681 -0.376 -0.222
## 1754.000 0.720 7.313 7.699 0.06% -0.010 0.635
## INTT_B 0.000 2.512 -1.161 0.27% -1.096 -0.934 -0.912
## 10355.000 2.893 8.153 13.915 0.01% -0.129 0.904
## INTT_1 0.000 2.416 -1.298 0.01% -1.130 -1.052 -0.986
## 9780.000 3.147 7.265 12.912 0.01% -0.139 0.905
## INTT_2 0.000 2.363 -1.697 0.01% -1.274 -1.119 -1.024
## 9519.000 3.840 7.067 15.034 0.01% -0.293 0.867
## INTT_3 0.000 2.123 -1.822 0.02% -1.459 -1.307 -0.672
## 8819.000 4.614 5.379 13.693 0.01% -0.398 1.370
## PUBT_B 0.000 0.125 -1.606 0.08% -0.935 0.035 0.053
## 8363.000 0.652 -0.580 3.081 0.01% 0.073 0.820
## PUBT_1 0.000 -0.042 -2.124 0.05% -0.843 -0.054 0.036
## 4224.000 0.636 -0.382 3.082 0.02% 0.157 0.790
## PUBT_2 0.000 -0.276 -2.839 0.01% -0.610 -0.151 0.041
## 9064.000 0.600 0.112 3.016 0.01% 0.232 0.660
## PUBT_3 0.000 -0.457 -2.940 0.01% -0.583 -0.036 0.115
## 8592.000 0.552 0.549 2.534 0.02% 0.252 0.554
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 69
##
## Loglikelihood
##
## H0 Value -129524.375
## H0 Scaling Correction Factor 2.6656
## for MLR
## H1 Value -129439.944
## H1 Scaling Correction Factor 2.3436
## for MLR
##
## Information Criteria
##
## Akaike (AIC) 259186.750
## Bayesian (BIC) 259686.717
## Sample-Size Adjusted BIC 259467.444
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 131.349*
## Degrees of Freedom 21
## P-Value 0.0000
## Scaling Correction Factor 1.2856
## for MLR
##
## * The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
## for chi-square difference testing in the regular way. MLM, MLR and WLSM
## chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
## and ULSMV difference testing is done using the DIFFTEST option.
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.023
## 90 Percent C.I. 0.019 0.026
## Probability RMSEA <= .05 1.000
##
## CFI/TLI
##
## CFI 0.993
## TLI 0.977
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 15223.002
## Degrees of Freedom 66
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.012
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_INTT BY
## INTT_B 1.000 0.000 999.000 999.000
## INTT_1 1.000 0.000 999.000 999.000
## INTT_2 1.000 0.000 999.000 999.000
## INTT_3 1.000 0.000 999.000 999.000
##
## RI_PUBTT BY
## PUBT_B 1.000 0.000 999.000 999.000
## PUBT_1 1.000 0.000 999.000 999.000
## PUBT_2 1.000 0.000 999.000 999.000
## PUBT_3 1.000 0.000 999.000 999.000
##
## RI_WCHRT BY
## WCHRT_B 1.000 0.000 999.000 999.000
## WCHRT_1 1.000 0.000 999.000 999.000
## WCHRT_2 1.000 0.000 999.000 999.000
## WCHRT_3 1.000 0.000 999.000 999.000
##
## WINTT_B BY
## INTT_B 1.000 0.000 999.000 999.000
##
## WINTT_1 BY
## INTT_1 1.000 0.000 999.000 999.000
##
## WINTT_2 BY
## INTT_2 1.000 0.000 999.000 999.000
##
## WINTT_3 BY
## INTT_3 1.000 0.000 999.000 999.000
##
## WWCHRT_B BY
## WCHRT_B 1.000 0.000 999.000 999.000
##
## WWCHRT_1 BY
## WCHRT_1 1.000 0.000 999.000 999.000
##
## WWCHRT_2 BY
## WCHRT_2 1.000 0.000 999.000 999.000
##
## WWCHRT_3 BY
## WCHRT_3 1.000 0.000 999.000 999.000
##
## WPUBT_B BY
## PUBT_B 1.000 0.000 999.000 999.000
##
## WPUBT_1 BY
## PUBT_1 1.000 0.000 999.000 999.000
##
## WPUBT_2 BY
## PUBT_2 1.000 0.000 999.000 999.000
##
## WPUBT_3 BY
## PUBT_3 1.000 0.000 999.000 999.000
##
## WWCHRT_1 ON
## WWCHRT_B -0.118 0.072 -1.643 0.100
## WINTT_B -0.023 0.012 -1.919 0.055
## WPUBT_B 0.005 0.013 0.359 0.719
##
## WWCHRT_2 ON
## WWCHRT_1 0.203 0.038 5.278 0.000
## WINTT_1 0.019 0.010 1.922 0.055
## WPUBT_1 0.014 0.018 0.794 0.427
##
## WWCHRT_3 ON
## WWCHRT_2 0.493 0.077 6.363 0.000
## WINTT_2 0.000 0.010 0.044 0.965
## WPUBT_2 -0.031 0.021 -1.509 0.131
##
## WPUBT_1 ON
## WPUBT_B 0.252 0.023 11.204 0.000
## WINTT_B -0.001 0.019 -0.064 0.949
## WWCHRT_B -0.018 0.092 -0.195 0.845
##
## WPUBT_2 ON
## WPUBT_1 0.368 0.020 18.189 0.000
## WINTT_1 -0.001 0.010 -0.058 0.953
## WWCHRT_1 -0.048 0.022 -2.236 0.025
##
## WPUBT_3 ON
## WPUBT_2 0.386 0.016 24.272 0.000
## WINTT_2 0.009 0.006 1.554 0.120
## WWCHRT_2 -0.028 0.018 -1.516 0.129
##
## WINTT_1 ON
## WINTT_B 0.056 0.047 1.177 0.239
## WPUBT_B 0.015 0.030 0.493 0.622
## WWCHRT_B 0.099 0.129 0.763 0.445
##
## WINTT_2 ON
## WINTT_1 0.232 0.036 6.368 0.000
## WPUBT_1 0.094 0.043 2.178 0.029
## WWCHRT_1 -0.040 0.046 -0.875 0.382
##
## WINTT_3 ON
## WINTT_2 0.425 0.025 16.708 0.000
## WPUBT_2 0.092 0.030 3.018 0.003
## WWCHRT_2 0.133 0.054 2.448 0.014
##
## RI_INTT WITH
## RI_PUBTT 0.040 0.013 3.050 0.002
## RI_WCHRT 0.128 0.014 9.100 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## RI_PUBTT WITH
## RI_WCHRT 0.071 0.005 13.510 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## WINTT_B WITH
## WPUBT_B 0.011 0.015 0.753 0.451
## WWCHRT_B -0.017 0.010 -1.705 0.088
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WWCHRT_B WITH
## WPUBT_B -0.005 0.006 -0.935 0.350
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WINTT_1 WITH
## WPUBT_1 0.003 0.021 0.139 0.889
## WWCHRT_1 0.006 0.015 0.374 0.708
##
## WPUBT_1 WITH
## WWCHRT_1 0.001 0.010 0.085 0.932
##
## WINTT_2 WITH
## WPUBT_2 0.018 0.012 1.454 0.146
## WWCHRT_2 0.017 0.010 1.655 0.098
##
## WPUBT_2 WITH
## WWCHRT_2 -0.011 0.004 -2.579 0.010
##
## WINTT_3 WITH
## WPUBT_3 0.043 0.011 3.869 0.000
## WWCHRT_3 0.030 0.022 1.378 0.168
##
## WPUBT_3 WITH
## WWCHRT_3 -0.003 0.008 -0.350 0.727
##
## RI_WCHRT WITH
## WPUBT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## WCHRT_B 0.000 0.007 0.042 0.967
## WCHRT_1 0.006 0.008 0.741 0.459
## WCHRT_2 0.016 0.008 1.979 0.048
## WCHRT_3 -0.070 0.014 -5.128 0.000
## INTT_B 0.001 0.017 0.041 0.967
## INTT_1 0.009 0.018 0.507 0.612
## INTT_2 0.011 0.020 0.532 0.595
## INTT_3 0.026 0.023 1.144 0.252
## PUBT_B 0.002 0.009 0.210 0.833
## PUBT_1 0.033 0.011 2.964 0.003
## PUBT_2 0.006 0.008 0.711 0.477
## PUBT_3 0.005 0.008 0.660 0.509
##
## Variances
## RI_INTT 1.846 0.070 26.308 0.000
## RI_PUBTT 0.111 0.009 12.019 0.000
## RI_WCHRT 0.388 0.010 40.198 0.000
## WINTT_B 1.112 0.066 16.745 0.000
## WWCHRT_B 0.101 0.012 8.705 0.000
## WPUBT_B 0.545 0.012 47.249 0.000
##
## Residual Variances
## WCHRT_B 0.000 0.000 999.000 999.000
## WCHRT_1 0.000 0.000 999.000 999.000
## WCHRT_2 0.000 0.000 999.000 999.000
## WCHRT_3 0.000 0.000 999.000 999.000
## INTT_B 0.000 0.000 999.000 999.000
## INTT_1 0.000 0.000 999.000 999.000
## INTT_2 0.000 0.000 999.000 999.000
## INTT_3 0.000 0.000 999.000 999.000
## PUBT_B 0.000 0.000 999.000 999.000
## PUBT_1 0.000 0.000 999.000 999.000
## PUBT_2 0.000 0.000 999.000 999.000
## PUBT_3 0.000 0.000 999.000 999.000
## WINTT_1 1.233 0.074 16.639 0.000
## WINTT_2 1.956 0.076 25.616 0.000
## WINTT_3 2.456 0.079 31.028 0.000
## WWCHRT_1 0.174 0.023 7.492 0.000
## WWCHRT_2 0.216 0.017 12.566 0.000
## WWCHRT_3 0.243 0.037 6.496 0.000
## WPUBT_1 0.483 0.013 38.455 0.000
## WPUBT_2 0.414 0.008 49.764 0.000
## WPUBT_3 0.371 0.008 47.376 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.437E-03
## (ratio of smallest to largest eigenvalue)
##
##
## STANDARDIZED MODEL RESULTS
##
##
## STDYX Standardization
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_INTT BY
## INTT_B 0.790 0.011 72.908 0.000
## INTT_1 0.774 0.013 60.440 0.000
## INTT_2 0.690 0.010 67.821 0.000
## INTT_3 0.628 0.010 63.556 0.000
##
## RI_PUBTT BY
## PUBT_B 0.412 0.017 24.370 0.000
## PUBT_1 0.421 0.018 22.833 0.000
## PUBT_2 0.432 0.018 23.838 0.000
## PUBT_3 0.448 0.018 24.375 0.000
##
## RI_WCHRT BY
## WCHRT_B 0.891 0.011 78.513 0.000
## WCHRT_1 0.829 0.018 47.219 0.000
## WCHRT_2 0.796 0.012 65.715 0.000
## WCHRT_3 0.752 0.025 30.629 0.000
##
## WINTT_B BY
## INTT_B 0.613 0.014 43.908 0.000
##
## WINTT_1 BY
## INTT_1 0.633 0.016 40.509 0.000
##
## WINTT_2 BY
## INTT_2 0.724 0.010 74.509 0.000
##
## WINTT_3 BY
## INTT_3 0.778 0.008 97.607 0.000
##
## WWCHRT_B BY
## WCHRT_B 0.454 0.022 20.347 0.000
##
## WWCHRT_1 BY
## WCHRT_1 0.559 0.026 21.452 0.000
##
## WWCHRT_2 BY
## WCHRT_2 0.605 0.016 37.922 0.000
##
## WWCHRT_3 BY
## WCHRT_3 0.659 0.028 23.523 0.000
##
## WPUBT_B BY
## PUBT_B 0.911 0.008 119.346 0.000
##
## WPUBT_1 BY
## PUBT_1 0.907 0.009 106.241 0.000
##
## WPUBT_2 BY
## PUBT_2 0.902 0.009 103.828 0.000
##
## WPUBT_3 BY
## PUBT_3 0.894 0.009 97.229 0.000
##
## WWCHRT_1 ON
## WWCHRT_B -0.089 0.054 -1.651 0.099
## WINTT_B -0.057 0.030 -1.906 0.057
## WPUBT_B 0.008 0.023 0.358 0.720
##
## WWCHRT_2 ON
## WWCHRT_1 0.180 0.032 5.611 0.000
## WINTT_1 0.045 0.024 1.870 0.062
## WPUBT_1 0.022 0.027 0.790 0.430
##
## WWCHRT_3 ON
## WWCHRT_2 0.427 0.060 7.139 0.000
## WINTT_2 0.001 0.025 0.044 0.965
## WPUBT_2 -0.040 0.026 -1.507 0.132
##
## WPUBT_1 ON
## WPUBT_B 0.259 0.022 11.761 0.000
## WINTT_B -0.002 0.028 -0.064 0.949
## WWCHRT_B -0.008 0.041 -0.195 0.845
##
## WPUBT_2 ON
## WPUBT_1 0.380 0.021 18.377 0.000
## WINTT_1 -0.001 0.016 -0.058 0.953
## WWCHRT_1 -0.029 0.013 -2.270 0.023
##
## WPUBT_3 ON
## WPUBT_2 0.404 0.016 25.515 0.000
## WINTT_2 0.020 0.013 1.552 0.121
## WWCHRT_2 -0.020 0.013 -1.528 0.127
##
## WINTT_1 ON
## WINTT_B 0.053 0.044 1.186 0.236
## WPUBT_B 0.010 0.020 0.493 0.622
## WWCHRT_B 0.028 0.037 0.766 0.443
##
## WINTT_2 ON
## WINTT_1 0.181 0.030 6.063 0.000
## WPUBT_1 0.048 0.022 2.172 0.030
## WWCHRT_1 -0.012 0.014 -0.873 0.383
##
## WINTT_3 ON
## WINTT_2 0.359 0.020 17.544 0.000
## WPUBT_2 0.038 0.013 3.030 0.002
## WWCHRT_2 0.037 0.015 2.493 0.013
##
## RI_INTT WITH
## RI_PUBTT 0.088 0.029 3.071 0.002
## RI_WCHRT 0.152 0.016 9.599 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## RI_PUBTT WITH
## RI_WCHRT 0.344 0.028 12.263 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## WINTT_B WITH
## WPUBT_B 0.014 0.019 0.753 0.451
## WWCHRT_B -0.051 0.031 -1.672 0.095
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WWCHRT_B WITH
## WPUBT_B -0.023 0.025 -0.929 0.353
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WINTT_1 WITH
## WPUBT_1 0.004 0.027 0.139 0.889
## WWCHRT_1 0.012 0.033 0.375 0.708
##
## WPUBT_1 WITH
## WWCHRT_1 0.003 0.036 0.085 0.932
##
## WINTT_2 WITH
## WPUBT_2 0.019 0.013 1.460 0.144
## WWCHRT_2 0.025 0.016 1.633 0.103
##
## WPUBT_2 WITH
## WWCHRT_2 -0.036 0.014 -2.646 0.008
##
## WINTT_3 WITH
## WPUBT_3 0.046 0.012 3.888 0.000
## WWCHRT_3 0.039 0.029 1.341 0.180
##
## WPUBT_3 WITH
## WWCHRT_3 -0.009 0.025 -0.348 0.728
##
## RI_WCHRT WITH
## WPUBT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## WCHRT_B 0.000 0.010 0.042 0.967
## WCHRT_1 0.007 0.010 0.746 0.456
## WCHRT_2 0.021 0.010 2.010 0.044
## WCHRT_3 -0.084 0.018 -4.737 0.000
## INTT_B 0.000 0.010 0.041 0.967
## INTT_1 0.005 0.010 0.510 0.610
## INTT_2 0.005 0.010 0.535 0.592
## INTT_3 0.012 0.010 1.158 0.247
## PUBT_B 0.002 0.011 0.210 0.833
## PUBT_1 0.042 0.014 2.964 0.003
## PUBT_2 0.007 0.010 0.710 0.478
## PUBT_3 0.007 0.011 0.659 0.510
##
## Variances
## RI_INTT 1.000 0.000 999.000 999.000
## RI_PUBTT 1.000 0.000 999.000 999.000
## RI_WCHRT 1.000 0.000 999.000 999.000
## WINTT_B 1.000 0.000 999.000 999.000
## WWCHRT_B 1.000 0.000 999.000 999.000
## WPUBT_B 1.000 0.000 999.000 999.000
##
## Residual Variances
## WCHRT_B 0.000 999.000 999.000 999.000
## WCHRT_1 0.000 999.000 999.000 999.000
## WCHRT_2 0.000 999.000 999.000 999.000
## WCHRT_3 0.000 999.000 999.000 999.000
## INTT_B 0.000 999.000 999.000 999.000
## INTT_1 0.000 999.000 999.000 999.000
## INTT_2 0.000 999.000 999.000 999.000
## INTT_3 0.000 999.000 999.000 999.000
## PUBT_B 0.000 999.000 999.000 999.000
## PUBT_1 0.000 999.000 999.000 999.000
## PUBT_2 0.000 999.000 999.000 999.000
## PUBT_3 0.000 999.000 999.000 999.000
## WINTT_1 0.996 0.005 185.214 0.000
## WINTT_2 0.965 0.011 86.719 0.000
## WINTT_3 0.866 0.015 57.831 0.000
## WWCHRT_1 0.989 0.010 96.054 0.000
## WWCHRT_2 0.965 0.012 80.925 0.000
## WWCHRT_3 0.815 0.051 16.046 0.000
## WPUBT_1 0.933 0.011 82.894 0.000
## WPUBT_2 0.855 0.016 54.703 0.000
## WPUBT_3 0.835 0.013 65.507 0.000
##
##
## R-SQUARE
##
## Observed Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WCHRT_B 1.000 999.000 999.000 999.000
## WCHRT_1 1.000 999.000 999.000 999.000
## WCHRT_2 1.000 999.000 999.000 999.000
## WCHRT_3 1.000 999.000 999.000 999.000
## INTT_B 1.000 999.000 999.000 999.000
## INTT_1 1.000 999.000 999.000 999.000
## INTT_2 1.000 999.000 999.000 999.000
## INTT_3 1.000 999.000 999.000 999.000
## PUBT_B 1.000 999.000 999.000 999.000
## PUBT_1 1.000 999.000 999.000 999.000
## PUBT_2 1.000 999.000 999.000 999.000
## PUBT_3 1.000 999.000 999.000 999.000
##
## Latent Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WINTT_1 0.004 0.005 0.651 0.515
## WINTT_2 0.035 0.011 3.175 0.002
## WINTT_3 0.134 0.015 8.926 0.000
## WWCHRT_1 0.011 0.010 1.045 0.296
## WWCHRT_2 0.035 0.012 2.930 0.003
## WWCHRT_3 0.185 0.051 3.647 0.000
## WPUBT_1 0.067 0.011 5.957 0.000
## WPUBT_2 0.145 0.016 9.304 0.000
## WPUBT_3 0.165 0.013 12.912 0.000
##
##
## MODEL MODIFICATION INDICES
##
## NOTE: Modification indices for direct effects of observed dependent variables
## regressed on covariates may not be included. To include these, request
## MODINDICES (ALL).
##
## Minimum M.I. value for printing the modification index 10.000
##
## M.I. E.P.C. Std E.P.C. StdYX E.P.C.
##
## BY Statements
##
## RI_INTT BY INTT_B 55.894 -0.229 -0.311 -0.181
## RI_INTT BY INTT_1 49.855 0.175 0.237 0.135
## WINTT_B BY INTT_3 41.521 -0.171 -0.180 -0.083
## WINTT_1 BY INTT_2 57.910 -0.438 -0.487 -0.248
## WINTT_1 BY INTT_3 58.958 0.188 0.209 0.097
## WINTT_3 BY INTT_B 53.905 -0.094 -0.158 -0.092
## WINTT_3 BY INTT_1 59.062 0.088 0.149 0.085
## WINTT_3 BY INTT_2 58.713 -0.638 -1.073 -0.545
## WWCHRT_B BY WCHRT_3 13.586 -0.247 -0.078 -0.094
## WWCHRT_1 BY WCHRT_2 13.890 -0.294 -0.123 -0.158
## WWCHRT_1 BY WCHRT_3 12.445 0.140 0.059 0.071
## WWCHRT_3 BY WCHRT_B 16.111 -0.132 -0.072 -0.103
## WWCHRT_3 BY WCHRT_1 12.424 0.103 0.056 0.075
## WWCHRT_3 BY WCHRT_2 13.810 -0.625 -0.341 -0.436
## WPUBT_B BY PUBT_3 20.208 -0.081 -0.060 -0.080
## WPUBT_1 BY PUBT_2 19.088 -0.234 -0.169 -0.218
## WPUBT_1 BY PUBT_3 18.620 0.090 0.065 0.087
## WPUBT_3 BY PUBT_B 29.114 -0.162 -0.108 -0.133
## WPUBT_3 BY PUBT_1 23.033 0.102 0.068 0.086
## WPUBT_3 BY PUBT_2 20.345 -0.284 -0.189 -0.245
##
## ON/BY Statements
##
## RI_INTT ON WINTT_B /
## WINTT_B BY RI_INTT 57.730 -0.407 -0.316 -0.316
## RI_INTT ON WINTT_1 /
## WINTT_1 BY RI_INTT 59.475 0.327 0.268 0.268
## RI_PUBTT ON WPUBT_B /
## WPUBT_B BY RI_PUBTT 18.555 -0.213 -0.471 -0.471
## RI_PUBTT ON WPUBT_1 /
## WPUBT_1 BY RI_PUBTT 18.068 0.144 0.311 0.311
## RI_WCHRT ON WWCHRT_B /
## WWCHRT_B BY RI_WCHRT 11.605 -0.478 -0.244 -0.244
## RI_WCHRT ON WWCHRT_1 /
## WWCHRT_1 BY RI_WCHRT 10.154 0.245 0.165 0.165
## WINTT_B ON RI_INTT /
## RI_INTT BY WINTT_B 55.625 -0.242 -0.312 -0.312
## WINTT_B ON WINTT_3 /
## WINTT_3 BY WINTT_B 52.051 -0.097 -0.154 -0.154
## WINTT_1 ON RI_INTT /
## RI_INTT BY WINTT_1 58.395 0.212 0.259 0.259
## WINTT_1 ON WINTT_3 /
## WINTT_3 BY WINTT_1 58.921 0.091 0.138 0.138
## WINTT_2 ON WINTT_3 /
## WINTT_3 BY WINTT_2 58.714 -0.638 -0.754 -0.754
## WINTT_3 ON WINTT_B /
## WINTT_B BY WINTT_3 41.521 -0.171 -0.107 -0.107
## WINTT_3 ON WINTT_1 /
## WINTT_1 BY WINTT_3 58.958 0.188 0.124 0.124
## WWCHRT_B ON WWCHRT_3 /
## WWCHRT_3 BY WWCHRT_B 15.988 -0.119 -0.204 -0.204
## WWCHRT_1 ON WWCHRT_3 /
## WWCHRT_3 BY WWCHRT_1 12.377 0.106 0.138 0.138
## WWCHRT_2 ON WWCHRT_3 /
## WWCHRT_3 BY WWCHRT_2 13.808 -0.625 -0.721 -0.721
## WWCHRT_3 ON WWCHRT_B /
## WWCHRT_B BY WWCHRT_3 13.586 -0.247 -0.143 -0.143
## WWCHRT_3 ON WWCHRT_1 /
## WWCHRT_1 BY WWCHRT_3 12.445 0.140 0.108 0.108
## WPUBT_B ON WPUBT_3 /
## WPUBT_3 BY WPUBT_B 25.762 -0.197 -0.178 -0.178
## WPUBT_1 ON RI_PUBTT /
## RI_PUBTT BY WPUBT_1 17.481 0.482 0.224 0.224
## WPUBT_1 ON WPUBT_3 /
## WPUBT_3 BY WPUBT_1 23.511 0.118 0.109 0.109
## WPUBT_2 ON WPUBT_3 /
## WPUBT_3 BY WPUBT_2 20.344 -0.284 -0.272 -0.272
## WPUBT_3 ON WPUBT_B /
## WPUBT_B BY WPUBT_3 20.208 -0.081 -0.090 -0.090
## WPUBT_3 ON WPUBT_1 /
## WPUBT_1 BY WPUBT_3 18.620 0.090 0.097 0.097
##
## WITH Statements
##
## WCHRT_2 WITH WCHRT_1 11.140 -0.046 -0.046 999.000
## WCHRT_3 WITH WCHRT_B 12.988 -0.026 -0.026 999.000
## WCHRT_3 WITH WCHRT_1 11.803 0.024 0.024 999.000
## WCHRT_3 WITH WCHRT_2 12.338 -0.144 -0.144 999.000
## INTT_2 WITH INTT_1 60.519 -0.525 -0.525 999.000
## INTT_3 WITH INTT_B 44.253 -0.190 -0.190 999.000
## INTT_3 WITH INTT_1 60.002 0.219 0.219 999.000
## INTT_3 WITH INTT_2 58.841 -1.566 -1.566 999.000
## PUBT_2 WITH PUBT_1 33.347 -0.110 -0.110 999.000
## PUBT_3 WITH PUBT_B 26.156 -0.044 -0.044 999.000
## PUBT_3 WITH PUBT_1 25.905 0.041 0.041 999.000
## PUBT_3 WITH PUBT_2 19.963 -0.105 -0.105 999.000
## WINTT_B WITH RI_INTT 59.046 -0.459 -0.320 -0.320
## WINTT_1 WITH RI_INTT 59.244 0.387 0.256 0.256
## WINTT_3 WITH WINTT_B 41.839 -0.191 -0.116 -0.116
## WINTT_3 WITH WINTT_1 59.887 0.227 0.130 0.130
## WINTT_3 WITH WINTT_2 58.840 -1.566 -0.714 -0.714
## WWCHRT_B WITH RI_WCHRT 11.423 -0.049 -0.247 -0.247
## WWCHRT_1 WITH RI_WCHRT 10.954 0.047 0.181 0.181
## WWCHRT_3 WITH WWCHRT_B 13.564 -0.025 -0.159 -0.159
## WWCHRT_3 WITH WWCHRT_1 11.776 0.025 0.122 0.122
## WWCHRT_3 WITH WWCHRT_2 12.338 -0.144 -0.628 -0.628
## WPUBT_B WITH RI_PUBTT 17.989 -0.114 -0.464 -0.464
## WPUBT_1 WITH RI_PUBTT 18.371 0.060 0.258 0.258
## WPUBT_3 WITH WPUBT_B 20.159 -0.044 -0.098 -0.098
## WPUBT_3 WITH WPUBT_1 26.807 0.048 0.113 0.113
## WPUBT_3 WITH WPUBT_2 19.961 -0.105 -0.267 -0.267
##
## Variances/Residual Variances
##
## WCHRT_2 15.782 0.323 0.323 0.528
## INTT_2 58.572 3.680 3.680 0.950
## PUBT_2 20.475 0.272 0.272 0.456
##
##
## TECHNICAL 4 OUTPUT
##
##
## ESTIMATES DERIVED FROM THE MODEL
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 1.846
## RI_PUBTT 0.040 0.111
## RI_WCHRT 0.128 0.071 0.388
## WINTT_B 0.000 0.000 0.000 1.112
## WINTT_1 0.000 0.000 0.000 0.060 1.237
## WINTT_2 0.000 0.000 0.000 0.015 0.287
## WINTT_3 0.000 0.000 0.000 0.006 0.125
## WWCHRT_B 0.000 0.000 0.000 -0.017 0.009
## WWCHRT_1 0.000 0.000 0.000 -0.023 0.003
## WWCHRT_2 0.000 0.000 0.000 -0.004 0.024
## WWCHRT_3 0.000 0.000 0.000 -0.002 0.012
## WPUBT_B 0.000 0.000 0.000 0.011 0.008
## WPUBT_1 0.000 0.000 0.000 0.002 0.005
## WPUBT_2 0.000 0.000 0.000 0.002 0.001
## WPUBT_3 0.000 0.000 0.000 0.001 0.002
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 2.028
## WINTT_3 0.868 2.835
## WWCHRT_B 0.002 0.001 0.101
## WWCHRT_1 -0.006 0.001 -0.011 0.176
## WWCHRT_2 0.021 0.038 -0.002 0.036 0.224
## WWCHRT_3 0.010 0.047 -0.001 0.018 0.111
## WPUBT_B 0.015 0.011 -0.005 0.003 0.003
## WPUBT_1 0.050 0.040 -0.003 0.002 0.008
## WPUBT_2 0.036 0.059 -0.001 -0.008 -0.010
## WPUBT_3 0.032 0.073 0.000 -0.004 -0.010
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.298
## WPUBT_B 0.000 0.545
## WPUBT_1 -0.002 0.137 0.518
## WPUBT_2 -0.020 0.050 0.190 0.485
## WPUBT_3 -0.013 0.020 0.074 0.188 0.444
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.070
## RI_PUBTT 0.013 0.009
## RI_WCHRT 0.014 0.005 0.010
## WINTT_B 0.000 0.000 0.000 0.066
## WINTT_1 0.000 0.000 0.000 0.054 0.079
## WINTT_2 0.000 0.000 0.000 0.014 0.057
## WINTT_3 0.000 0.000 0.000 0.006 0.028
## WWCHRT_B 0.000 0.000 0.000 0.010 0.013
## WWCHRT_1 0.000 0.000 0.000 0.012 0.014
## WWCHRT_2 0.000 0.000 0.000 0.003 0.014
## WWCHRT_3 0.000 0.000 0.000 0.002 0.008
## WPUBT_B 0.000 0.000 0.000 0.015 0.017
## WPUBT_1 0.000 0.000 0.000 0.023 0.023
## WPUBT_2 0.000 0.000 0.000 0.009 0.016
## WPUBT_3 0.000 0.000 0.000 0.003 0.007
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.087
## WINTT_3 0.070 0.105
## WWCHRT_B 0.003 0.002 0.012
## WWCHRT_1 0.010 0.005 0.007 0.023
## WWCHRT_2 0.012 0.014 0.001 0.007 0.018
## WWCHRT_3 0.021 0.025 0.001 0.005 0.020
## WPUBT_B 0.008 0.005 0.006 0.007 0.003
## WPUBT_1 0.026 0.014 0.010 0.010 0.011
## WPUBT_2 0.016 0.018 0.004 0.005 0.006
## WPUBT_3 0.015 0.016 0.001 0.002 0.005
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.044
## WPUBT_B 0.002 0.012
## WPUBT_1 0.007 0.014 0.016
## WPUBT_2 0.011 0.007 0.014 0.013
## WPUBT_3 0.009 0.003 0.008 0.011 0.012
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 26.308
## RI_PUBTT 3.050 12.019
## RI_WCHRT 9.100 13.510 40.198
## WINTT_B 0.000 0.000 0.000 16.745
## WINTT_1 0.000 0.000 0.000 1.111 15.675
## WINTT_2 0.000 0.000 0.000 1.097 5.061
## WINTT_3 0.000 0.000 0.000 0.977 4.489
## WWCHRT_B 0.000 0.000 0.000 -1.705 0.675
## WWCHRT_1 0.000 0.000 0.000 -1.935 0.232
## WWCHRT_2 0.000 0.000 0.000 -1.118 1.679
## WWCHRT_3 0.000 0.000 0.000 -1.110 1.528
## WPUBT_B 0.000 0.000 0.000 0.753 0.484
## WPUBT_1 0.000 0.000 0.000 0.077 0.200
## WPUBT_2 0.000 0.000 0.000 0.204 0.053
## WPUBT_3 0.000 0.000 0.000 0.273 0.350
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 23.432
## WINTT_3 12.336 26.984
## WWCHRT_B 0.677 0.378 8.705
## WWCHRT_1 -0.632 0.283 -1.759 7.796
## WWCHRT_2 1.795 2.727 -1.659 4.958 12.766
## WWCHRT_3 0.502 1.866 -1.624 3.580 5.531
## WPUBT_B 1.746 2.490 -0.935 0.442 0.826
## WPUBT_1 1.926 2.821 -0.317 0.180 0.720
## WPUBT_2 2.223 3.179 -0.167 -1.484 -1.582
## WPUBT_3 2.183 4.671 -0.108 -1.821 -1.934
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 6.788
## WPUBT_B -0.109 47.249
## WPUBT_1 -0.282 9.916 32.237
## WPUBT_2 -1.841 7.353 13.525 38.391
## WPUBT_3 -1.468 6.171 9.795 17.103 36.710
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.002 0.000
## RI_WCHRT 0.000 0.000 0.000
## WINTT_B 1.000 1.000 1.000 0.000
## WINTT_1 1.000 1.000 1.000 0.266 0.000
## WINTT_2 1.000 1.000 1.000 0.272 0.000
## WINTT_3 1.000 1.000 1.000 0.329 0.000
## WWCHRT_B 1.000 1.000 1.000 0.088 0.500
## WWCHRT_1 1.000 1.000 1.000 0.053 0.816
## WWCHRT_2 1.000 1.000 1.000 0.264 0.093
## WWCHRT_3 1.000 1.000 1.000 0.267 0.127
## WPUBT_B 1.000 1.000 1.000 0.451 0.629
## WPUBT_1 1.000 1.000 1.000 0.938 0.841
## WPUBT_2 1.000 1.000 1.000 0.839 0.958
## WPUBT_3 1.000 1.000 1.000 0.785 0.727
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.000 0.000
## WWCHRT_B 0.499 0.706 0.000
## WWCHRT_1 0.527 0.777 0.079 0.000
## WWCHRT_2 0.073 0.006 0.097 0.000 0.000
## WWCHRT_3 0.616 0.062 0.104 0.000 0.000
## WPUBT_B 0.081 0.013 0.350 0.659 0.409
## WPUBT_1 0.054 0.005 0.751 0.857 0.471
## WPUBT_2 0.026 0.001 0.867 0.138 0.114
## WPUBT_3 0.029 0.000 0.914 0.069 0.053
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.913 0.000
## WPUBT_1 0.778 0.000 0.000
## WPUBT_2 0.066 0.000 0.000 0.000
## WPUBT_3 0.142 0.000 0.000 0.000 0.000
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 1.000
## RI_PUBTT 0.088 1.000
## RI_WCHRT 0.152 0.344 1.000
## WINTT_B 0.000 0.000 0.000 1.000
## WINTT_1 0.000 0.000 0.000 0.051 1.000
## WINTT_2 0.000 0.000 0.000 0.010 0.181
## WINTT_3 0.000 0.000 0.000 0.003 0.067
## WWCHRT_B 0.000 0.000 0.000 -0.051 0.025
## WWCHRT_1 0.000 0.000 0.000 -0.053 0.007
## WWCHRT_2 0.000 0.000 0.000 -0.007 0.046
## WWCHRT_3 0.000 0.000 0.000 -0.003 0.020
## WPUBT_B 0.000 0.000 0.000 0.014 0.010
## WPUBT_1 0.000 0.000 0.000 0.002 0.006
## WPUBT_2 0.000 0.000 0.000 0.002 0.001
## WPUBT_3 0.000 0.000 0.000 0.001 0.003
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 1.000
## WINTT_3 0.362 1.000
## WWCHRT_B 0.005 0.001 1.000
## WWCHRT_1 -0.010 0.002 -0.086 1.000
## WWCHRT_2 0.032 0.048 -0.015 0.180 1.000
## WWCHRT_3 0.013 0.051 -0.006 0.078 0.429
## WPUBT_B 0.014 0.009 -0.023 0.009 0.008
## WPUBT_1 0.049 0.033 -0.014 0.006 0.023
## WPUBT_2 0.036 0.050 -0.003 -0.027 -0.029
## WPUBT_3 0.034 0.065 -0.001 -0.015 -0.031
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 1.000
## WPUBT_B -0.001 1.000
## WPUBT_1 -0.005 0.259 1.000
## WPUBT_2 -0.052 0.098 0.380 1.000
## WPUBT_3 -0.037 0.040 0.154 0.405 1.000
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.029 0.000
## RI_WCHRT 0.016 0.028 0.000
## WINTT_B 0.000 0.000 0.000 0.000
## WINTT_1 0.000 0.000 0.000 0.044 0.000
## WINTT_2 0.000 0.000 0.000 0.009 0.030
## WINTT_3 0.000 0.000 0.000 0.003 0.013
## WWCHRT_B 0.000 0.000 0.000 0.031 0.037
## WWCHRT_1 0.000 0.000 0.000 0.028 0.030
## WWCHRT_2 0.000 0.000 0.000 0.006 0.028
## WWCHRT_3 0.000 0.000 0.000 0.003 0.013
## WPUBT_B 0.000 0.000 0.000 0.019 0.020
## WPUBT_1 0.000 0.000 0.000 0.030 0.029
## WPUBT_2 0.000 0.000 0.000 0.012 0.020
## WPUBT_3 0.000 0.000 0.000 0.005 0.009
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.021 0.000
## WWCHRT_B 0.007 0.003 0.000
## WWCHRT_1 0.016 0.007 0.053 0.000
## WWCHRT_2 0.018 0.018 0.009 0.032 0.000
## WWCHRT_3 0.027 0.029 0.004 0.018 0.060
## WPUBT_B 0.008 0.004 0.025 0.022 0.009
## WPUBT_1 0.025 0.011 0.043 0.034 0.032
## WPUBT_2 0.016 0.016 0.016 0.018 0.018
## WPUBT_3 0.016 0.014 0.007 0.008 0.016
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.005 0.000
## WPUBT_1 0.018 0.022 0.000
## WPUBT_2 0.028 0.012 0.021 0.000
## WPUBT_3 0.025 0.006 0.013 0.016 0.000
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 999.000
## RI_PUBTT 3.071 999.000
## RI_WCHRT 9.599 12.263 999.000
## WINTT_B 0.000 0.000 0.000 999.000
## WINTT_1 0.000 0.000 0.000 1.164 999.000
## WINTT_2 0.000 0.000 0.000 1.120 6.062
## WINTT_3 0.000 0.000 0.000 0.992 5.123
## WWCHRT_B 0.000 0.000 0.000 -1.672 0.681
## WWCHRT_1 0.000 0.000 0.000 -1.898 0.233
## WWCHRT_2 0.000 0.000 0.000 -1.115 1.672
## WWCHRT_3 0.000 0.000 0.000 -1.110 1.547
## WPUBT_B 0.000 0.000 0.000 0.753 0.484
## WPUBT_1 0.000 0.000 0.000 0.077 0.201
## WPUBT_2 0.000 0.000 0.000 0.204 0.053
## WPUBT_3 0.000 0.000 0.000 0.273 0.350
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 999.000
## WINTT_3 17.618 999.000
## WWCHRT_B 0.679 0.379 999.000
## WWCHRT_1 -0.635 0.283 -1.616 999.000
## WWCHRT_2 1.766 2.689 -1.584 5.586 999.000
## WWCHRT_3 0.499 1.790 -1.559 4.222 7.175
## WPUBT_B 1.754 2.519 -0.929 0.440 0.822
## WPUBT_1 1.933 2.859 -0.317 0.181 0.718
## WPUBT_2 2.238 3.200 -0.167 -1.484 -1.605
## WPUBT_3 2.194 4.719 -0.108 -1.837 -1.943
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 999.000
## WPUBT_B -0.109 999.000
## WPUBT_1 -0.282 11.839 999.000
## WPUBT_2 -1.841 8.299 18.529 999.000
## WPUBT_3 -1.439 6.815 12.168 25.682 999.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.002 0.000
## RI_WCHRT 0.000 0.000 0.000
## WINTT_B 1.000 1.000 1.000 0.000
## WINTT_1 1.000 1.000 1.000 0.244 0.000
## WINTT_2 1.000 1.000 1.000 0.263 0.000
## WINTT_3 1.000 1.000 1.000 0.321 0.000
## WWCHRT_B 1.000 1.000 1.000 0.095 0.496
## WWCHRT_1 1.000 1.000 1.000 0.058 0.816
## WWCHRT_2 1.000 1.000 1.000 0.265 0.095
## WWCHRT_3 1.000 1.000 1.000 0.267 0.122
## WPUBT_B 1.000 1.000 1.000 0.451 0.628
## WPUBT_1 1.000 1.000 1.000 0.938 0.841
## WPUBT_2 1.000 1.000 1.000 0.839 0.958
## WPUBT_3 1.000 1.000 1.000 0.785 0.726
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.000 0.000
## WWCHRT_B 0.497 0.705 0.000
## WWCHRT_1 0.526 0.777 0.106 0.000
## WWCHRT_2 0.077 0.007 0.113 0.000 0.000
## WWCHRT_3 0.618 0.073 0.119 0.000 0.000
## WPUBT_B 0.080 0.012 0.353 0.660 0.411
## WPUBT_1 0.053 0.004 0.751 0.857 0.472
## WPUBT_2 0.025 0.001 0.867 0.138 0.108
## WPUBT_3 0.028 0.000 0.914 0.066 0.052
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.913 0.000
## WPUBT_1 0.778 0.000 0.000
## WPUBT_2 0.066 0.000 0.000 0.000
## WPUBT_3 0.150 0.000 0.000 0.000 0.000
##
##
## DIAGRAM INFORMATION
##
## Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram.
## If running Mplus from the Mplus Diagrammer, the diagram opens automatically.
##
## Diagram output
## c:\users\ocrobert\onedrive - indiana university\abcd\obesity week\mplus\all full model.dgm
##
## Beginning Time: 14:02:22
## Ending Time: 14:02:23
## Elapsed Time: 00:00:01
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2023 Muthen & Muthen
# Specify the path to your Mplus output file
output_path <- "C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Obesity Week/Mplus/all full model boys.out"
# Read and print the file
output_content <- readLines(output_path)
cat(output_content, sep = "\n")
## Mplus VERSION 8.10
## MUTHEN & MUTHEN
## 07/01/2024 2:18 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Full Sample Model
## DATA: FILE = "Fullsample_race3_sex_subset.dat";
## VARIABLE:
## NAMES = sex race3 age_B int_B intt_B age_1 int_1 intt_1
## age_2 int_2 intt_2 age_3 int_3 intt_3 wchr_B wchrt_B
## wchr_1 wchrt_1 wchr_2 wchrt_2 wchr_3 wchrt_3 pub_B pubt_B
## pub_1 pubt_1 pub_2 pubt_2 pub_3 pubt_3 int_p_ave famc_ave bw_lbs matage
## matalc_ave matcig_ave matmar_ave ses_lt;
## MISSING=.;
##
## USEVARIABLES ARE
## wchrt_b wchrt_1
## wchrt_2 wchrt_3
## intt_b intt_1
## intt_2 intt_3
## pubt_b pubt_1
## pubt_2 pubt_3;
##
## USEOBSERVATIONS ARE (Sex EQ 1);
##
## DEFINE:
## wchrt_b = wchrt_b*10;
## wchrt_1 = wchrt_1*10;
## wchrt_2 = wchrt_2*10;
## wchrt_3 = wchrt_3*10;
##
## ANALYSIS:
## ESTIMATOR=MLR;
## MODEL=NOCOVARIANCES;
##
## MODEL:
## ! Estimate random inttercept)
## RI_intt BY intt_b@1 intt_1@1 intt_2@1 intt_3@1;
## RI_pubtT BY pubt_b@1 pubt_1@1 pubt_2@1 pubt_3@1;
## RI_wchrt BY wchrt_b@1 wchrt_1@1 wchrt_2@1 wchrt_3@1;
##
## ! RI correlations
## RI_intt with RI_pubtT RI_wchrt;
## RI_pubtT with RI_wchrt;
##
## ! Create within-person centered variables
## wintt_b BY intt_b@1;
## wintt_1 BY intt_1@1;
## wintt_2 BY intt_2@1;
## wintt_3 BY intt_3@1;
##
## wwchrt_b BY wchrt_b@1;
## wwchrt_1 BY wchrt_1@1;
## wwchrt_2 BY wchrt_2@1;
## wwchrt_3 BY wchrt_3@1;
##
## wpubt_b BY pubt_b@1;
## wpubt_1 BY pubt_1@1;
## wpubt_2 BY pubt_2@1;
## wpubt_3 BY pubt_3@1;
##
## ! Constrain the measurement error variances to zero
## intt_b@0;
## intt_1@0;
## intt_2@0;
## intt_3@0;
##
## wchrt_b@0;
## wchrt_1@0;
## wchrt_2@0;
## wchrt_3@0;
##
## pubt_b@0;
## pubt_1@0;
## pubt_2@0;
## pubt_3@0;
##
## ! Estimate the Lagged Effects
## wwchrt_1 ON wwchrt_b;
## wwchrt_1 ON wintt_b;
## wwchrt_1 ON wpubt_b;
##
## wwchrt_2 ON wwchrt_1;
## wwchrt_2 ON wintt_1;
## wwchrt_2 ON wpubt_1;
##
## wwchrt_3 ON wwchrt_2;
## wwchrt_3 ON wintt_2;
## wwchrt_3 ON wpubt_2;
##
## wpubt_1 ON wpubt_b;
## wpubt_1 ON wintt_b;
## wpubt_1 ON wwchrt_b;
##
## wpubt_2 ON wpubt_1;
## wpubt_2 ON wintt_1;
## wpubt_2 ON wwchrt_1;
##
## wpubt_3 ON wpubt_2;
## wpubt_3 ON wintt_2;
## wpubt_3 ON wwchrt_2;
##
## wintt_1 ON wintt_b;
## wintt_1 ON wpubt_b;
## wintt_1 ON wwchrt_b;
##
## wintt_2 ON wintt_1;
## wintt_2 ON wpubt_1;
## wintt_2 ON wwchrt_1;
##
## wintt_3 ON wintt_2;
## wintt_3 ON wpubt_2;
## wintt_3 ON wwchrt_2;
##
## ! Estimate the covariance between the within-person
## ! centered variables at the first wave
## wintt_b with wpubt_b;
## wwchrt_b with wintt_b wpubt_b;
##
## ! Estimate covariances between residuals of within-person components
## ! (i.e., innovations)
## wintt_1 with wpubt_1 wwchrt_1;
## wpubt_1 with wwchrt_1;
##
## wintt_2 with wpubt_2 wwchrt_2;
## wpubt_2 with wwchrt_2;
##
## wintt_3 with wpubt_3 wwchrt_3;
## wpubt_3 with wwchrt_3;
##
## ! ask for variances for all variables that are included;
## [intt_b intt_1 intt_2 intt_3];
## [pubt_b pubt_1 pubt_2 pubt_3];
## [wchrt_b wchrt_1 wchrt_2 wchrt_3];
##
## ! Fix the correlation between the individual factors and the other
## ! exogenous variables to zero (by default these would be estimated)
## RI_wchrt with wwchrt_b@0 wintt_b@0 wpubt_b@0;
## RI_intt with wwchrt_b@0 wintt_b@0 wpubt_b@0;
## RI_pubtT with wwchrt_b@0 wintt_b@0 wpubt_b@0;
##
## OUTPUT: STDYX MODINDICES Tech4;
##
##
##
## *** WARNING
## Data set contains cases with missing on all variables.
## These cases were not included in the analysis.
## Number of cases with missing on all variables: 1
## 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
##
##
##
## Full Sample Model
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 5417
##
## Number of dependent variables 12
## Number of independent variables 0
## Number of continuous latent variables 15
##
## Observed dependent variables
##
## Continuous
## WCHRT_B WCHRT_1 WCHRT_2 WCHRT_3 INTT_B INTT_1
## INTT_2 INTT_3 PUBT_B PUBT_1 PUBT_2 PUBT_3
##
## Continuous latent variables
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1 WINTT_2
## WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2 WWCHRT_3 WPUBT_B
## WPUBT_1 WPUBT_2 WPUBT_3
##
##
## Estimator MLR
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Fullsample_race3_sex_subset.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 136
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.100
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## WCHRT_B WCHRT_1 WCHRT_2 WCHRT_3 INTT_B
## ________ ________ ________ ________ ________
## WCHRT_B 0.999
## WCHRT_1 0.939 0.940
## WCHRT_2 0.775 0.756 0.776
## WCHRT_3 0.170 0.166 0.157 0.170
## INTT_B 0.998 0.940 0.776 0.170 1.000
## INTT_1 0.946 0.939 0.759 0.167 0.946
## INTT_2 0.924 0.896 0.774 0.167 0.925
## INTT_3 0.860 0.834 0.721 0.170 0.860
## PUBT_B 0.884 0.832 0.697 0.162 0.884
## PUBT_1 0.421 0.418 0.407 0.107 0.421
## PUBT_2 0.906 0.880 0.759 0.164 0.907
## PUBT_3 0.857 0.832 0.715 0.165 0.858
##
##
## Covariance Coverage
## INTT_1 INTT_2 INTT_3 PUBT_B PUBT_1
## ________ ________ ________ ________ ________
## INTT_1 0.947
## INTT_2 0.902 0.925
## INTT_3 0.840 0.841 0.861
## PUBT_B 0.838 0.820 0.765 0.885
## PUBT_1 0.421 0.411 0.396 0.408 0.421
## PUBT_2 0.885 0.903 0.823 0.807 0.406
## PUBT_3 0.838 0.836 0.843 0.765 0.397
##
##
## Covariance Coverage
## PUBT_2 PUBT_3
## ________ ________
## PUBT_2 0.907
## PUBT_3 0.823 0.858
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## WCHRT_B 0.000 1.217 -1.812 0.02% -0.540 -0.283 -0.150
## 5409.000 0.465 3.097 6.647 0.02% 0.008 0.483
## WCHRT_1 0.000 1.555 -1.970 0.02% -0.571 -0.305 -0.172
## 5092.000 0.518 7.309 8.991 0.02% -0.006 0.536
## WCHRT_2 0.000 1.809 -1.811 0.02% -0.619 -0.332 -0.185
## 4203.000 0.601 10.489 9.219 0.02% 0.005 0.567
## WCHRT_3 0.000 1.652 -1.326 0.11% -0.664 -0.397 -0.240
## 921.000 0.702 6.257 7.060 0.11% -0.031 0.640
## INTT_B 0.000 2.404 -1.161 0.52% -1.125 -1.092 -1.074
## 5415.000 3.170 7.582 13.915 0.02% -0.132 0.893
## INTT_1 0.000 2.317 -1.298 0.02% -1.181 -1.088 -1.055
## 5129.000 3.320 6.509 12.777 0.02% -0.181 0.878
## INTT_2 0.000 2.332 -1.546 0.06% -1.266 -1.119 -1.045
## 5013.000 3.804 6.777 13.867 0.02% -0.281 0.867
## INTT_3 0.000 2.032 -1.558 0.02% -1.372 -1.263 -0.503
## 4662.000 4.011 4.776 13.693 0.02% -0.328 0.792
## PUBT_B 0.000 0.427 -0.967 0.29% -0.933 0.043 0.053
## 4792.000 0.584 -0.183 3.081 0.02% 0.065 1.037
## PUBT_1 0.000 0.315 -1.129 0.22% -0.873 -0.069 0.021
## 2280.000 0.575 -0.570 3.082 0.04% 0.097 0.901
## PUBT_2 0.000 0.068 -1.858 0.02% -0.790 -0.210 -0.016
## 4915.000 0.621 -0.498 3.016 0.02% 0.178 0.725
## PUBT_3 0.000 -0.198 -2.150 0.04% -0.694 -0.112 0.040
## 4650.000 0.656 -0.272 2.534 0.04% 0.230 0.686
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 69
##
## Loglikelihood
##
## H0 Value -68328.736
## H0 Scaling Correction Factor 2.5100
## for MLR
## H1 Value -68283.222
## H1 Scaling Correction Factor 2.2056
## for MLR
##
## Information Criteria
##
## Akaike (AIC) 136795.472
## Bayesian (BIC) 137250.685
## Sample-Size Adjusted BIC 137031.425
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 75.508*
## Degrees of Freedom 21
## P-Value 0.0000
## Scaling Correction Factor 1.2055
## for MLR
##
## * The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
## for chi-square difference testing in the regular way. MLM, MLR and WLSM
## chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
## and ULSMV difference testing is done using the DIFFTEST option.
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.022
## 90 Percent C.I. 0.017 0.027
## Probability RMSEA <= .05 1.000
##
## CFI/TLI
##
## CFI 0.994
## TLI 0.980
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 8796.875
## Degrees of Freedom 66
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.014
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_INTT BY
## INTT_B 1.000 0.000 999.000 999.000
## INTT_1 1.000 0.000 999.000 999.000
## INTT_2 1.000 0.000 999.000 999.000
## INTT_3 1.000 0.000 999.000 999.000
##
## RI_PUBTT BY
## PUBT_B 1.000 0.000 999.000 999.000
## PUBT_1 1.000 0.000 999.000 999.000
## PUBT_2 1.000 0.000 999.000 999.000
## PUBT_3 1.000 0.000 999.000 999.000
##
## RI_WCHRT BY
## WCHRT_B 1.000 0.000 999.000 999.000
## WCHRT_1 1.000 0.000 999.000 999.000
## WCHRT_2 1.000 0.000 999.000 999.000
## WCHRT_3 1.000 0.000 999.000 999.000
##
## WINTT_B BY
## INTT_B 1.000 0.000 999.000 999.000
##
## WINTT_1 BY
## INTT_1 1.000 0.000 999.000 999.000
##
## WINTT_2 BY
## INTT_2 1.000 0.000 999.000 999.000
##
## WINTT_3 BY
## INTT_3 1.000 0.000 999.000 999.000
##
## WWCHRT_B BY
## WCHRT_B 1.000 0.000 999.000 999.000
##
## WWCHRT_1 BY
## WCHRT_1 1.000 0.000 999.000 999.000
##
## WWCHRT_2 BY
## WCHRT_2 1.000 0.000 999.000 999.000
##
## WWCHRT_3 BY
## WCHRT_3 1.000 0.000 999.000 999.000
##
## WPUBT_B BY
## PUBT_B 1.000 0.000 999.000 999.000
##
## WPUBT_1 BY
## PUBT_1 1.000 0.000 999.000 999.000
##
## WPUBT_2 BY
## PUBT_2 1.000 0.000 999.000 999.000
##
## WPUBT_3 BY
## PUBT_3 1.000 0.000 999.000 999.000
##
## WWCHRT_1 ON
## WWCHRT_B -0.275 0.135 -2.034 0.042
## WINTT_B -0.011 0.015 -0.737 0.461
## WPUBT_B -0.038 0.021 -1.860 0.063
##
## WWCHRT_2 ON
## WWCHRT_1 0.334 0.059 5.682 0.000
## WINTT_1 0.026 0.013 2.027 0.043
## WPUBT_1 -0.025 0.023 -1.073 0.283
##
## WWCHRT_3 ON
## WWCHRT_2 0.502 0.050 10.106 0.000
## WINTT_2 -0.024 0.012 -1.967 0.049
## WPUBT_2 -0.087 0.025 -3.510 0.000
##
## WPUBT_1 ON
## WPUBT_B 0.174 0.033 5.291 0.000
## WINTT_B -0.008 0.021 -0.369 0.712
## WWCHRT_B 0.019 0.156 0.124 0.902
##
## WPUBT_2 ON
## WPUBT_1 0.326 0.030 10.877 0.000
## WINTT_1 0.015 0.014 1.101 0.271
## WWCHRT_1 -0.069 0.037 -1.851 0.064
##
## WPUBT_3 ON
## WPUBT_2 0.410 0.020 20.778 0.000
## WINTT_2 0.008 0.010 0.862 0.389
## WWCHRT_2 -0.064 0.026 -2.469 0.014
##
## WINTT_1 ON
## WINTT_B 0.089 0.052 1.704 0.088
## WPUBT_B -0.024 0.039 -0.621 0.534
## WWCHRT_B 0.287 0.229 1.253 0.210
##
## WINTT_2 ON
## WINTT_1 0.196 0.047 4.196 0.000
## WPUBT_1 -0.029 0.058 -0.499 0.617
## WWCHRT_1 0.122 0.074 1.655 0.098
##
## WINTT_3 ON
## WINTT_2 0.337 0.034 9.962 0.000
## WPUBT_2 0.008 0.037 0.210 0.834
## WWCHRT_2 0.110 0.063 1.764 0.078
##
## RI_INTT WITH
## RI_PUBTT 0.047 0.017 2.786 0.005
## RI_WCHRT 0.120 0.018 6.489 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## RI_PUBTT WITH
## RI_WCHRT 0.059 0.007 8.497 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## WINTT_B WITH
## WPUBT_B -0.004 0.019 -0.195 0.845
## WWCHRT_B -0.017 0.015 -1.161 0.246
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WWCHRT_B WITH
## WPUBT_B -0.023 0.008 -2.954 0.003
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WINTT_1 WITH
## WPUBT_1 -0.051 0.025 -2.021 0.043
## WWCHRT_1 0.035 0.022 1.643 0.100
##
## WPUBT_1 WITH
## WWCHRT_1 -0.007 0.015 -0.473 0.636
##
## WINTT_2 WITH
## WPUBT_2 -0.015 0.017 -0.861 0.389
## WWCHRT_2 0.011 0.014 0.784 0.433
##
## WPUBT_2 WITH
## WWCHRT_2 -0.022 0.006 -3.855 0.000
##
## WINTT_3 WITH
## WPUBT_3 0.029 0.016 1.863 0.063
## WWCHRT_3 -0.002 0.027 -0.079 0.937
##
## WPUBT_3 WITH
## WWCHRT_3 -0.013 0.010 -1.328 0.184
##
## RI_WCHRT WITH
## WPUBT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## WCHRT_B 0.000 0.009 0.032 0.974
## WCHRT_1 0.007 0.010 0.681 0.496
## WCHRT_2 0.014 0.011 1.269 0.204
## WCHRT_3 -0.060 0.018 -3.274 0.001
## INTT_B 0.001 0.024 0.021 0.983
## INTT_1 0.010 0.025 0.413 0.679
## INTT_2 0.010 0.027 0.381 0.703
## INTT_3 0.012 0.029 0.403 0.687
## PUBT_B 0.002 0.011 0.146 0.884
## PUBT_1 0.019 0.015 1.248 0.212
## PUBT_2 0.003 0.011 0.242 0.809
## PUBT_3 0.003 0.012 0.224 0.823
##
## Variances
## RI_INTT 1.941 0.095 20.370 0.000
## RI_PUBTT 0.099 0.012 8.589 0.000
## RI_WCHRT 0.388 0.013 28.904 0.000
## WINTT_B 1.306 0.097 13.443 0.000
## WWCHRT_B 0.077 0.010 7.532 0.000
## WPUBT_B 0.492 0.015 32.328 0.000
##
## Residual Variances
## WCHRT_B 0.000 0.000 999.000 999.000
## WCHRT_1 0.000 0.000 999.000 999.000
## WCHRT_2 0.000 0.000 999.000 999.000
## WCHRT_3 0.000 0.000 999.000 999.000
## INTT_B 0.000 0.000 999.000 999.000
## INTT_1 0.000 0.000 999.000 999.000
## INTT_2 0.000 0.000 999.000 999.000
## INTT_3 0.000 0.000 999.000 999.000
## PUBT_B 0.000 0.000 999.000 999.000
## PUBT_1 0.000 0.000 999.000 999.000
## PUBT_2 0.000 0.000 999.000 999.000
## PUBT_3 0.000 0.000 999.000 999.000
## WINTT_1 1.286 0.090 14.229 0.000
## WINTT_2 1.803 0.096 18.699 0.000
## WINTT_3 1.919 0.083 23.002 0.000
## WWCHRT_1 0.125 0.022 5.585 0.000
## WWCHRT_2 0.227 0.031 7.451 0.000
## WWCHRT_3 0.211 0.050 4.222 0.000
## WPUBT_1 0.458 0.016 28.440 0.000
## WPUBT_2 0.467 0.012 40.479 0.000
## WPUBT_3 0.465 0.011 42.516 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.151E-03
## (ratio of smallest to largest eigenvalue)
##
##
## STANDARDIZED MODEL RESULTS
##
##
## STDYX Standardization
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_INTT BY
## INTT_B 0.773 0.014 55.578 0.000
## INTT_1 0.774 0.015 50.259 0.000
## INTT_2 0.715 0.013 54.953 0.000
## INTT_3 0.690 0.012 55.719 0.000
##
## RI_PUBTT BY
## PUBT_B 0.410 0.024 17.332 0.000
## PUBT_1 0.417 0.025 16.424 0.000
## PUBT_2 0.401 0.023 17.134 0.000
## PUBT_3 0.390 0.023 17.329 0.000
##
## RI_WCHRT BY
## WCHRT_B 0.914 0.011 82.570 0.000
## WCHRT_1 0.865 0.018 48.121 0.000
## WCHRT_2 0.784 0.021 37.561 0.000
## WCHRT_3 0.763 0.026 29.123 0.000
##
## WINTT_B BY
## INTT_B 0.634 0.017 37.389 0.000
##
## WINTT_1 BY
## INTT_1 0.634 0.019 33.728 0.000
##
## WINTT_2 BY
## INTT_2 0.699 0.013 52.587 0.000
##
## WINTT_3 BY
## INTT_3 0.724 0.012 61.269 0.000
##
## WWCHRT_B BY
## WCHRT_B 0.406 0.025 16.320 0.000
##
## WWCHRT_1 BY
## WCHRT_1 0.502 0.031 16.201 0.000
##
## WWCHRT_2 BY
## WCHRT_2 0.621 0.026 23.580 0.000
##
## WWCHRT_3 BY
## WCHRT_3 0.647 0.031 20.967 0.000
##
## WPUBT_B BY
## PUBT_B 0.912 0.011 85.663 0.000
##
## WPUBT_1 BY
## PUBT_1 0.909 0.012 78.101 0.000
##
## WPUBT_2 BY
## PUBT_2 0.916 0.010 89.268 0.000
##
## WPUBT_3 BY
## PUBT_3 0.921 0.010 96.629 0.000
##
## WWCHRT_1 ON
## WWCHRT_B -0.210 0.102 -2.053 0.040
## WINTT_B -0.035 0.047 -0.732 0.464
## WPUBT_B -0.074 0.040 -1.858 0.063
##
## WWCHRT_2 ON
## WWCHRT_1 0.244 0.047 5.167 0.000
## WINTT_1 0.060 0.032 1.913 0.056
## WPUBT_1 -0.034 0.031 -1.093 0.275
##
## WWCHRT_3 ON
## WWCHRT_2 0.469 0.076 6.150 0.000
## WINTT_2 -0.061 0.031 -1.953 0.051
## WPUBT_2 -0.118 0.032 -3.703 0.000
##
## WPUBT_1 ON
## WPUBT_B 0.177 0.033 5.429 0.000
## WINTT_B -0.013 0.035 -0.370 0.712
## WWCHRT_B 0.008 0.063 0.124 0.902
##
## WPUBT_2 ON
## WPUBT_1 0.311 0.029 10.681 0.000
## WINTT_1 0.024 0.022 1.099 0.272
## WWCHRT_1 -0.035 0.018 -1.901 0.057
##
## WPUBT_3 ON
## WPUBT_2 0.397 0.018 21.486 0.000
## WINTT_2 0.015 0.018 0.862 0.389
## WWCHRT_2 -0.042 0.017 -2.544 0.011
##
## WINTT_1 ON
## WINTT_B 0.089 0.052 1.726 0.084
## WPUBT_B -0.015 0.024 -0.621 0.534
## WWCHRT_B 0.070 0.055 1.264 0.206
##
## WINTT_2 ON
## WINTT_1 0.164 0.040 4.103 0.000
## WPUBT_1 -0.015 0.029 -0.499 0.618
## WWCHRT_1 0.032 0.019 1.672 0.095
##
## WINTT_3 ON
## WINTT_2 0.314 0.030 10.348 0.000
## WPUBT_2 0.004 0.018 0.210 0.834
## WWCHRT_2 0.037 0.020 1.825 0.068
##
## RI_INTT WITH
## RI_PUBTT 0.107 0.039 2.748 0.006
## RI_WCHRT 0.138 0.020 6.835 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## RI_PUBTT WITH
## RI_WCHRT 0.301 0.041 7.394 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## WINTT_B WITH
## WPUBT_B -0.005 0.023 -0.195 0.845
## WWCHRT_B -0.055 0.049 -1.134 0.257
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WWCHRT_B WITH
## WPUBT_B -0.117 0.040 -2.901 0.004
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WINTT_1 WITH
## WPUBT_1 -0.067 0.033 -2.027 0.043
## WWCHRT_1 0.088 0.053 1.660 0.097
##
## WPUBT_1 WITH
## WWCHRT_1 -0.030 0.063 -0.474 0.635
##
## WINTT_2 WITH
## WPUBT_2 -0.016 0.018 -0.861 0.389
## WWCHRT_2 0.017 0.022 0.769 0.442
##
## WPUBT_2 WITH
## WWCHRT_2 -0.068 0.016 -4.176 0.000
##
## WINTT_3 WITH
## WPUBT_3 0.031 0.017 1.867 0.062
## WWCHRT_3 -0.003 0.043 -0.079 0.937
##
## WPUBT_3 WITH
## WWCHRT_3 -0.042 0.033 -1.285 0.199
##
## RI_WCHRT WITH
## WPUBT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## WCHRT_B 0.000 0.014 0.032 0.974
## WCHRT_1 0.009 0.014 0.686 0.493
## WCHRT_2 0.018 0.014 1.291 0.197
## WCHRT_3 -0.074 0.024 -3.061 0.002
## INTT_B 0.000 0.013 0.021 0.983
## INTT_1 0.006 0.014 0.416 0.678
## INTT_2 0.005 0.014 0.383 0.702
## INTT_3 0.006 0.014 0.406 0.685
## PUBT_B 0.002 0.014 0.146 0.884
## PUBT_1 0.025 0.020 1.253 0.210
## PUBT_2 0.003 0.014 0.242 0.809
## PUBT_3 0.003 0.014 0.224 0.823
##
## Variances
## RI_INTT 1.000 0.000 999.000 999.000
## RI_PUBTT 1.000 0.000 999.000 999.000
## RI_WCHRT 1.000 0.000 999.000 999.000
## WINTT_B 1.000 0.000 999.000 999.000
## WWCHRT_B 1.000 0.000 999.000 999.000
## WPUBT_B 1.000 0.000 999.000 999.000
##
## Residual Variances
## WCHRT_B 0.000 999.000 999.000 999.000
## WCHRT_1 0.000 999.000 999.000 999.000
## WCHRT_2 0.000 999.000 999.000 999.000
## WCHRT_3 0.000 999.000 999.000 999.000
## INTT_B 0.000 999.000 999.000 999.000
## INTT_1 0.000 999.000 999.000 999.000
## INTT_2 0.000 999.000 999.000 999.000
## INTT_3 0.000 999.000 999.000 999.000
## PUBT_B 0.000 999.000 999.000 999.000
## PUBT_1 0.000 999.000 999.000 999.000
## PUBT_2 0.000 999.000 999.000 999.000
## PUBT_3 0.000 999.000 999.000 999.000
## WINTT_1 0.987 0.013 76.375 0.000
## WINTT_2 0.971 0.013 73.019 0.000
## WINTT_3 0.899 0.019 46.956 0.000
## WWCHRT_1 0.953 0.043 22.395 0.000
## WWCHRT_2 0.932 0.024 38.293 0.000
## WWCHRT_3 0.756 0.073 10.408 0.000
## WPUBT_1 0.969 0.011 84.887 0.000
## WPUBT_2 0.902 0.018 49.951 0.000
## WPUBT_3 0.838 0.015 57.068 0.000
##
##
## R-SQUARE
##
## Observed Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WCHRT_B 1.000 999.000 999.000 999.000
## WCHRT_1 1.000 999.000 999.000 999.000
## WCHRT_2 1.000 999.000 999.000 999.000
## WCHRT_3 1.000 999.000 999.000 999.000
## INTT_B 1.000 999.000 999.000 999.000
## INTT_1 1.000 999.000 999.000 999.000
## INTT_2 1.000 999.000 999.000 999.000
## INTT_3 1.000 999.000 999.000 999.000
## PUBT_B 1.000 999.000 999.000 999.000
## PUBT_1 1.000 999.000 999.000 999.000
## PUBT_2 1.000 999.000 999.000 999.000
## PUBT_3 1.000 999.000 999.000 999.000
##
## Latent Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WINTT_1 0.013 0.013 0.975 0.329
## WINTT_2 0.029 0.013 2.209 0.027
## WINTT_3 0.101 0.019 5.271 0.000
## WWCHRT_1 0.047 0.043 1.093 0.274
## WWCHRT_2 0.068 0.024 2.773 0.006
## WWCHRT_3 0.244 0.073 3.366 0.001
## WPUBT_1 0.031 0.011 2.745 0.006
## WPUBT_2 0.098 0.018 5.447 0.000
## WPUBT_3 0.162 0.015 11.017 0.000
##
##
## MODEL MODIFICATION INDICES
##
## NOTE: Modification indices for direct effects of observed dependent variables
## regressed on covariates may not be included. To include these, request
## MODINDICES (ALL).
##
## Minimum M.I. value for printing the modification index 10.000
##
## M.I. E.P.C. Std E.P.C. StdYX E.P.C.
##
## BY Statements
##
## RI_INTT BY INTT_B 24.005 -0.161 -0.224 -0.124
## RI_INTT BY INTT_1 20.965 0.124 0.172 0.096
## RI_PUBTT BY PUBT_B 17.862 -0.675 -0.213 -0.277
## RI_WCHRT BY PUBT_B 14.496 -0.128 -0.080 -0.104
## WINTT_B BY INTT_3 26.678 -0.154 -0.175 -0.087
## WINTT_1 BY INTT_2 23.076 -0.444 -0.506 -0.260
## WINTT_1 BY INTT_3 24.885 0.157 0.179 0.089
## WINTT_3 BY INTT_B 30.637 -0.117 -0.171 -0.095
## WINTT_3 BY INTT_1 26.270 0.099 0.145 0.080
## WINTT_3 BY INTT_2 24.723 -0.721 -1.054 -0.541
## WWCHRT_2 BY PUBT_B 12.726 -0.137 -0.068 -0.088
## WWCHRT_2 BY PUBT_1 12.966 0.778 0.384 0.508
## WPUBT_B BY WCHRT_1 13.527 0.199 0.140 0.194
## WPUBT_B BY WCHRT_2 10.433 -0.053 -0.037 -0.046
##
## ON/BY Statements
##
## RI_INTT ON WINTT_B /
## WINTT_B BY RI_INTT 25.384 -0.269 -0.221 -0.221
## RI_INTT ON WINTT_1 /
## WINTT_1 BY RI_INTT 25.529 0.239 0.196 0.196
## RI_PUBTT ON WWCHRT_2 /
## WWCHRT_2 BY RI_PUBTT 12.668 -0.166 -0.260 -0.260
## RI_WCHRT ON WPUBT_B /
## WPUBT_B BY RI_WCHRT 13.418 -0.101 -0.114 -0.114
## WINTT_B ON RI_INTT /
## RI_INTT BY WINTT_B 24.136 -0.179 -0.218 -0.218
## WINTT_B ON WINTT_3 /
## WINTT_3 BY WINTT_B 30.051 -0.125 -0.160 -0.160
## WINTT_1 ON RI_INTT /
## RI_INTT BY WINTT_1 25.658 0.150 0.183 0.183
## WINTT_1 ON WINTT_3 /
## WINTT_3 BY WINTT_1 26.135 0.102 0.131 0.131
## WINTT_2 ON WINTT_3 /
## WINTT_3 BY WINTT_2 24.725 -0.721 -0.773 -0.773
## WINTT_3 ON WINTT_B /
## WINTT_B BY WINTT_3 26.678 -0.154 -0.120 -0.120
## WINTT_3 ON WINTT_1 /
## WINTT_1 BY WINTT_3 24.885 0.157 0.122 0.122
## WWCHRT_2 ON WPUBT_B /
## WPUBT_B BY WWCHRT_2 11.942 -0.064 -0.090 -0.090
## WPUBT_B ON RI_PUBTT /
## RI_PUBTT BY WPUBT_B 17.655 -0.750 -0.337 -0.337
## WPUBT_B ON RI_WCHRT /
## RI_WCHRT BY WPUBT_B 14.641 -0.134 -0.120 -0.120
## WPUBT_B ON WWCHRT_2 /
## WWCHRT_2 BY WPUBT_B 12.701 -0.142 -0.100 -0.100
## WPUBT_1 ON WWCHRT_2 /
## WWCHRT_2 BY WPUBT_1 12.967 0.778 0.559 0.559
##
## WITH Statements
##
## INTT_2 WITH INTT_1 27.249 -0.580 -0.580 999.000
## INTT_3 WITH INTT_B 28.259 -0.195 -0.195 999.000
## INTT_3 WITH INTT_1 26.918 0.193 0.193 999.000
## INTT_3 WITH INTT_2 25.155 -1.397 -1.397 999.000
## PUBT_B WITH WCHRT_1 13.803 0.096 0.096 999.000
## PUBT_B WITH WCHRT_2 10.751 -0.026 -0.026 999.000
## PUBT_1 WITH WCHRT_1 13.852 -0.362 -0.362 999.000
## PUBT_2 WITH PUBT_1 12.010 -0.098 -0.098 999.000
## WINTT_B WITH RI_INTT 26.249 -0.360 -0.226 -0.226
## WINTT_1 WITH RI_INTT 25.925 0.290 0.183 0.183
## WINTT_3 WITH WINTT_B 26.970 -0.202 -0.128 -0.128
## WINTT_3 WITH WINTT_1 26.784 0.199 0.127 0.127
## WINTT_3 WITH WINTT_2 25.157 -1.397 -0.751 -0.751
## WPUBT_B WITH RI_WCHRT 14.009 -0.051 -0.117 -0.117
## WPUBT_B WITH WWCHRT_2 12.087 -0.031 -0.094 -0.094
## WPUBT_1 WITH WWCHRT_2 12.337 0.173 0.535 0.535
##
## Variances/Residual Variances
##
## INTT_2 22.727 3.899 3.899 1.026
##
##
## TECHNICAL 4 OUTPUT
##
##
## ESTIMATES DERIVED FROM THE MODEL
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 1.941
## RI_PUBTT 0.047 0.099
## RI_WCHRT 0.120 0.059 0.388
## WINTT_B 0.000 0.000 0.000 1.306
## WINTT_1 0.000 0.000 0.000 0.111 1.303
## WINTT_2 0.000 0.000 0.000 0.021 0.261
## WINTT_3 0.000 0.000 0.000 0.007 0.093
## WWCHRT_B 0.000 0.000 0.000 -0.017 0.021
## WWCHRT_1 0.000 0.000 0.000 -0.009 0.029
## WWCHRT_2 0.000 0.000 0.000 0.000 0.045
## WWCHRT_3 0.000 0.000 0.000 0.000 0.017
## WPUBT_B 0.000 0.000 0.000 -0.004 -0.019
## WPUBT_1 0.000 0.000 0.000 -0.011 -0.055
## WPUBT_2 0.000 0.000 0.000 -0.001 0.000
## WPUBT_3 0.000 0.000 0.000 0.000 -0.001
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 1.858
## WINTT_3 0.628 2.135
## WWCHRT_B 0.002 0.000 0.077
## WWCHRT_1 0.022 0.012 -0.020 0.131
## WWCHRT_2 0.026 0.035 -0.006 0.045 0.244
## WWCHRT_3 -0.029 0.001 -0.003 0.023 0.124
## WPUBT_B -0.008 -0.003 -0.023 -0.013 -0.007
## WPUBT_1 -0.026 -0.009 -0.002 -0.010 -0.016
## WPUBT_2 -0.020 -0.006 0.001 -0.012 -0.030
## WPUBT_3 0.005 0.030 0.001 -0.007 -0.028
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.280
## WPUBT_B -0.006 0.492
## WPUBT_1 -0.021 0.085 0.473
## WPUBT_2 -0.060 0.028 0.154 0.518
## WPUBT_3 -0.046 0.012 0.064 0.214 0.555
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.095
## RI_PUBTT 0.017 0.012
## RI_WCHRT 0.018 0.007 0.013
## WINTT_B 0.000 0.000 0.000 0.097
## WINTT_1 0.000 0.000 0.000 0.073 0.101
## WINTT_2 0.000 0.000 0.000 0.017 0.072
## WINTT_3 0.000 0.000 0.000 0.006 0.029
## WWCHRT_B 0.000 0.000 0.000 0.015 0.018
## WWCHRT_1 0.000 0.000 0.000 0.016 0.018
## WWCHRT_2 0.000 0.000 0.000 0.006 0.021
## WWCHRT_3 0.000 0.000 0.000 0.003 0.011
## WPUBT_B 0.000 0.000 0.000 0.019 0.020
## WPUBT_1 0.000 0.000 0.000 0.028 0.028
## WPUBT_2 0.000 0.000 0.000 0.010 0.021
## WPUBT_3 0.000 0.000 0.000 0.004 0.010
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.108
## WINTT_3 0.082 0.109
## WWCHRT_B 0.004 0.002 0.010
## WWCHRT_1 0.011 0.005 0.009 0.020
## WWCHRT_2 0.016 0.017 0.003 0.011 0.032
## WWCHRT_3 0.024 0.032 0.002 0.006 0.021
## WPUBT_B 0.007 0.003 0.008 0.008 0.004
## WPUBT_1 0.030 0.013 0.012 0.013 0.014
## WPUBT_2 0.022 0.023 0.004 0.006 0.008
## WPUBT_3 0.021 0.021 0.002 0.003 0.007
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.045
## WPUBT_B 0.002 0.015
## WPUBT_1 0.008 0.017 0.019
## WPUBT_2 0.013 0.007 0.018 0.015
## WPUBT_3 0.012 0.003 0.009 0.014 0.016
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 20.370
## RI_PUBTT 2.786 8.589
## RI_WCHRT 6.489 8.497 28.904
## WINTT_B 0.000 0.000 0.000 13.443
## WINTT_1 0.000 0.000 0.000 1.526 12.927
## WINTT_2 0.000 0.000 0.000 1.247 3.623
## WINTT_3 0.000 0.000 0.000 1.167 3.186
## WWCHRT_B 0.000 0.000 0.000 -1.161 1.163
## WWCHRT_1 0.000 0.000 0.000 -0.599 1.591
## WWCHRT_2 0.000 0.000 0.000 0.007 2.141
## WWCHRT_3 0.000 0.000 0.000 -0.108 1.468
## WPUBT_B 0.000 0.000 0.000 -0.195 -0.959
## WPUBT_1 0.000 0.000 0.000 -0.395 -1.957
## WPUBT_2 0.000 0.000 0.000 -0.133 -0.011
## WPUBT_3 0.000 0.000 0.000 -0.088 -0.083
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 17.168
## WINTT_3 7.710 19.542
## WWCHRT_B 0.419 -0.045 7.532
## WWCHRT_1 2.001 2.367 -2.251 6.510
## WWCHRT_2 1.568 2.079 -2.008 3.968 7.664
## WWCHRT_3 -1.201 0.041 -1.927 3.572 5.817
## WPUBT_B -1.067 -1.029 -2.954 -1.501 -1.644
## WPUBT_1 -0.847 -0.711 -0.186 -0.741 -1.169
## WPUBT_2 -0.944 -0.265 0.226 -1.920 -3.698
## WPUBT_3 0.256 1.411 0.447 -2.499 -3.756
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 6.170
## WPUBT_B -2.326 32.328
## WPUBT_1 -2.486 4.910 24.459
## WPUBT_2 -4.423 3.906 8.665 33.835
## WPUBT_3 -3.687 3.582 7.025 15.244 35.011
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.005 0.000
## RI_WCHRT 0.000 0.000 0.000
## WINTT_B 1.000 1.000 1.000 0.000
## WINTT_1 1.000 1.000 1.000 0.127 0.000
## WINTT_2 1.000 1.000 1.000 0.212 0.000
## WINTT_3 1.000 1.000 1.000 0.243 0.001
## WWCHRT_B 1.000 1.000 1.000 0.246 0.245
## WWCHRT_1 1.000 1.000 1.000 0.549 0.112
## WWCHRT_2 1.000 1.000 1.000 0.994 0.032
## WWCHRT_3 1.000 1.000 1.000 0.914 0.142
## WPUBT_B 1.000 1.000 1.000 0.845 0.337
## WPUBT_1 1.000 1.000 1.000 0.693 0.050
## WPUBT_2 1.000 1.000 1.000 0.894 0.991
## WPUBT_3 1.000 1.000 1.000 0.930 0.934
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.000 0.000
## WWCHRT_B 0.676 0.964 0.000
## WWCHRT_1 0.045 0.018 0.024 0.000
## WWCHRT_2 0.117 0.038 0.045 0.000 0.000
## WWCHRT_3 0.230 0.968 0.054 0.000 0.000
## WPUBT_B 0.286 0.303 0.003 0.133 0.100
## WPUBT_1 0.397 0.477 0.852 0.458 0.242
## WPUBT_2 0.345 0.791 0.821 0.055 0.000
## WPUBT_3 0.798 0.158 0.655 0.012 0.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.020 0.000
## WPUBT_1 0.013 0.000 0.000
## WPUBT_2 0.000 0.000 0.000 0.000
## WPUBT_3 0.000 0.000 0.000 0.000 0.000
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 1.000
## RI_PUBTT 0.107 1.000
## RI_WCHRT 0.138 0.301 1.000
## WINTT_B 0.000 0.000 0.000 1.000
## WINTT_1 0.000 0.000 0.000 0.085 1.000
## WINTT_2 0.000 0.000 0.000 0.014 0.168
## WINTT_3 0.000 0.000 0.000 0.004 0.056
## WWCHRT_B 0.000 0.000 0.000 -0.055 0.067
## WWCHRT_1 0.000 0.000 0.000 -0.023 0.071
## WWCHRT_2 0.000 0.000 0.000 0.000 0.080
## WWCHRT_3 0.000 0.000 0.000 -0.001 0.027
## WPUBT_B 0.000 0.000 0.000 -0.005 -0.023
## WPUBT_1 0.000 0.000 0.000 -0.014 -0.070
## WPUBT_2 0.000 0.000 0.000 -0.002 0.000
## WPUBT_3 0.000 0.000 0.000 0.000 -0.001
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 1.000
## WINTT_3 0.316 1.000
## WWCHRT_B 0.005 0.000 1.000
## WWCHRT_1 0.045 0.023 -0.200 1.000
## WWCHRT_2 0.038 0.049 -0.044 0.250 1.000
## WWCHRT_3 -0.040 0.002 -0.022 0.120 0.476
## WPUBT_B -0.008 -0.003 -0.117 -0.050 -0.020
## WPUBT_1 -0.027 -0.009 -0.012 -0.039 -0.048
## WPUBT_2 -0.021 -0.006 0.005 -0.045 -0.084
## WPUBT_3 0.005 0.028 0.004 -0.028 -0.075
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 1.000
## WPUBT_B -0.015 1.000
## WPUBT_1 -0.058 0.176 1.000
## WPUBT_2 -0.156 0.056 0.311 1.000
## WPUBT_3 -0.116 0.023 0.125 0.400 1.000
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.039 0.000
## RI_WCHRT 0.020 0.041 0.000
## WINTT_B 0.000 0.000 0.000 0.000
## WINTT_1 0.000 0.000 0.000 0.052 0.000
## WINTT_2 0.000 0.000 0.000 0.011 0.040
## WINTT_3 0.000 0.000 0.000 0.004 0.016
## WWCHRT_B 0.000 0.000 0.000 0.049 0.056
## WWCHRT_1 0.000 0.000 0.000 0.038 0.043
## WWCHRT_2 0.000 0.000 0.000 0.011 0.038
## WWCHRT_3 0.000 0.000 0.000 0.005 0.019
## WPUBT_B 0.000 0.000 0.000 0.023 0.024
## WPUBT_1 0.000 0.000 0.000 0.036 0.036
## WPUBT_2 0.000 0.000 0.000 0.012 0.026
## WPUBT_3 0.000 0.000 0.000 0.005 0.011
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.030 0.000
## WWCHRT_B 0.011 0.004 0.000
## WWCHRT_1 0.022 0.009 0.100 0.000
## WWCHRT_2 0.025 0.024 0.024 0.048 0.000
## WWCHRT_3 0.034 0.041 0.012 0.030 0.076
## WPUBT_B 0.007 0.003 0.040 0.033 0.011
## WPUBT_1 0.032 0.013 0.065 0.052 0.040
## WPUBT_2 0.022 0.022 0.021 0.023 0.021
## WPUBT_3 0.021 0.019 0.009 0.011 0.019
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.006 0.000
## WPUBT_1 0.023 0.032 0.000
## WPUBT_2 0.034 0.013 0.029 0.000
## WPUBT_3 0.033 0.006 0.015 0.018 0.000
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 999.000
## RI_PUBTT 2.748 999.000
## RI_WCHRT 6.835 7.394 999.000
## WINTT_B 0.000 0.000 0.000 999.000
## WINTT_1 0.000 0.000 0.000 1.651 999.000
## WINTT_2 0.000 0.000 0.000 1.283 4.216
## WINTT_3 0.000 0.000 0.000 1.191 3.546
## WWCHRT_B 0.000 0.000 0.000 -1.134 1.185
## WWCHRT_1 0.000 0.000 0.000 -0.592 1.634
## WWCHRT_2 0.000 0.000 0.000 0.007 2.090
## WWCHRT_3 0.000 0.000 0.000 -0.108 1.455
## WPUBT_B 0.000 0.000 0.000 -0.195 -0.963
## WPUBT_1 0.000 0.000 0.000 -0.395 -1.966
## WPUBT_2 0.000 0.000 0.000 -0.133 -0.011
## WPUBT_3 0.000 0.000 0.000 -0.088 -0.083
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 999.000
## WINTT_3 10.416 999.000
## WWCHRT_B 0.421 -0.045 999.000
## WWCHRT_1 2.029 2.481 -1.990 999.000
## WWCHRT_2 1.513 2.012 -1.858 5.245 999.000
## WWCHRT_3 -1.201 0.040 -1.793 4.027 6.253
## WPUBT_B -1.071 -1.033 -2.901 -1.521 -1.715
## WPUBT_1 -0.848 -0.712 -0.186 -0.744 -1.191
## WPUBT_2 -0.943 -0.265 0.226 -1.928 -4.067
## WPUBT_3 0.256 1.412 0.446 -2.606 -3.921
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 999.000
## WPUBT_B -2.379 999.000
## WPUBT_1 -2.527 5.509 999.000
## WPUBT_2 -4.598 4.216 10.755 999.000
## WPUBT_3 -3.551 3.825 8.321 21.768 999.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.006 0.000
## RI_WCHRT 0.000 0.000 0.000
## WINTT_B 1.000 1.000 1.000 0.000
## WINTT_1 1.000 1.000 1.000 0.099 0.000
## WINTT_2 1.000 1.000 1.000 0.199 0.000
## WINTT_3 1.000 1.000 1.000 0.233 0.000
## WWCHRT_B 1.000 1.000 1.000 0.257 0.236
## WWCHRT_1 1.000 1.000 1.000 0.554 0.102
## WWCHRT_2 1.000 1.000 1.000 0.994 0.037
## WWCHRT_3 1.000 1.000 1.000 0.914 0.146
## WPUBT_B 1.000 1.000 1.000 0.845 0.336
## WPUBT_1 1.000 1.000 1.000 0.693 0.049
## WPUBT_2 1.000 1.000 1.000 0.894 0.991
## WPUBT_3 1.000 1.000 1.000 0.930 0.934
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.000 0.000
## WWCHRT_B 0.674 0.964 0.000
## WWCHRT_1 0.043 0.013 0.047 0.000
## WWCHRT_2 0.130 0.044 0.063 0.000 0.000
## WWCHRT_3 0.230 0.968 0.073 0.000 0.000
## WPUBT_B 0.284 0.301 0.004 0.128 0.086
## WPUBT_1 0.396 0.476 0.852 0.457 0.234
## WPUBT_2 0.346 0.791 0.822 0.054 0.000
## WPUBT_3 0.798 0.158 0.656 0.009 0.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.017 0.000
## WPUBT_1 0.012 0.000 0.000
## WPUBT_2 0.000 0.000 0.000 0.000
## WPUBT_3 0.000 0.000 0.000 0.000 0.000
##
##
## DIAGRAM INFORMATION
##
## Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram.
## If running Mplus from the Mplus Diagrammer, the diagram opens automatically.
##
## Diagram output
## c:\users\ocrobert\onedrive - indiana university\abcd\obesity week\mplus\all full model boys.dgm
##
## Beginning Time: 14:18:22
## Ending Time: 14:18:23
## Elapsed Time: 00:00:01
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2023 Muthen & Muthen
# Specify the path to your Mplus output file
output_path <- "C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Obesity Week/Mplus/all full model girls.out"
# Read and print the file
output_content <- readLines(output_path)
cat(output_content, sep = "\n")
## Mplus VERSION 8.10
## MUTHEN & MUTHEN
## 07/01/2024 2:24 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Full Sample Model
## DATA: FILE = "Fullsample_race3_sex_subset.dat";
## VARIABLE:
## NAMES = sex race3 age_B int_B intt_B age_1 int_1 intt_1
## age_2 int_2 intt_2 age_3 int_3 intt_3 wchr_B wchrt_B
## wchr_1 wchrt_1 wchr_2 wchrt_2 wchr_3 wchrt_3 pub_B pubt_B
## pub_1 pubt_1 pub_2 pubt_2 pub_3 pubt_3 int_p_ave famc_ave bw_lbs matage
## matalc_ave matcig_ave matmar_ave ses_lt;
## MISSING=.;
##
## USEVARIABLES ARE
## wchrt_b wchrt_1
## wchrt_2 wchrt_3
## intt_b intt_1
## intt_2 intt_3
## pubt_b pubt_1
## pubt_2 pubt_3;
##
## USEOBSERVATIONS ARE (Sex EQ 2);
##
## DEFINE:
## wchrt_b = wchrt_b*10;
## wchrt_1 = wchrt_1*10;
## wchrt_2 = wchrt_2*10;
## wchrt_3 = wchrt_3*10;
##
## ANALYSIS:
## ESTIMATOR=MLR;
## MODEL=NOCOVARIANCES;
## COVERAGE = 0;
##
## MODEL:
## ! Estimate random inttercept)
## RI_intt BY intt_b@1 intt_1@1 intt_2@1 intt_3@1;
## RI_pubtT BY pubt_b@1 pubt_1@1 pubt_2@1 pubt_3@1;
## RI_wchrt BY wchrt_b@1 wchrt_1@1 wchrt_2@1 wchrt_3@1;
##
## ! RI correlations
## RI_intt with RI_pubtT RI_wchrt;
## RI_pubtT with RI_wchrt;
##
## ! Create within-person centered variables
## wintt_b BY intt_b@1;
## wintt_1 BY intt_1@1;
## wintt_2 BY intt_2@1;
## wintt_3 BY intt_3@1;
##
## wwchrt_b BY wchrt_b@1;
## wwchrt_1 BY wchrt_1@1;
## wwchrt_2 BY wchrt_2@1;
## wwchrt_3 BY wchrt_3@1;
##
## wpubt_b BY pubt_b@1;
## wpubt_1 BY pubt_1@1;
## wpubt_2 BY pubt_2@1;
## wpubt_3 BY pubt_3@1;
##
## ! Constrain the measurement error variances to zero
## intt_b@0;
## intt_1@0;
## intt_2@0;
## intt_3@0;
##
## wchrt_b@0;
## wchrt_1@0;
## wchrt_2@0;
## wchrt_3@0;
##
## pubt_b@0;
## pubt_1@0;
## pubt_2@0;
## pubt_3@0;
##
## ! Estimate the Lagged Effects
## wwchrt_1 ON wwchrt_b;
## wwchrt_1 ON wintt_b;
## wwchrt_1 ON wpubt_b;
##
## wwchrt_2 ON wwchrt_1;
## wwchrt_2 ON wintt_1;
## wwchrt_2 ON wpubt_1;
##
## wwchrt_3 ON wwchrt_2;
## wwchrt_3 ON wintt_2;
## wwchrt_3 ON wpubt_2;
##
## wpubt_1 ON wpubt_b;
## wpubt_1 ON wintt_b;
## wpubt_1 ON wwchrt_b;
##
## wpubt_2 ON wpubt_1;
## wpubt_2 ON wintt_1;
## wpubt_2 ON wwchrt_1;
##
## wpubt_3 ON wpubt_2;
## wpubt_3 ON wintt_2;
## wpubt_3 ON wwchrt_2;
##
## wintt_1 ON wintt_b;
## wintt_1 ON wpubt_b;
## wintt_1 ON wwchrt_b;
##
## wintt_2 ON wintt_1;
## wintt_2 ON wpubt_1;
## wintt_2 ON wwchrt_1;
##
## wintt_3 ON wintt_2;
## wintt_3 ON wpubt_2;
## wintt_3 ON wwchrt_2;
##
## ! Estimate the covariance between the within-person
## ! centered variables at the first wave
## wintt_b with wpubt_b;
## wwchrt_b with wintt_b wpubt_b;
##
## ! Estimate covariances between residuals of within-person components
## ! (i.e., innovations)
## wintt_1 with wpubt_1 wwchrt_1;
## wpubt_1 with wwchrt_1;
##
## wintt_2 with wpubt_2 wwchrt_2;
## wpubt_2 with wwchrt_2;
##
## wintt_3 with wpubt_3 wwchrt_3;
## wpubt_3 with wwchrt_3;
##
## ! ask for variances for all variables that are included;
## [intt_b intt_1 intt_2 intt_3];
## [pubt_b pubt_1 pubt_2 pubt_3];
## [wchrt_b wchrt_1 wchrt_2 wchrt_3];
##
## ! Fix the correlation between the individual factors and the other
## ! exogenous variables to zero (by default these would be estimated)
## RI_wchrt with wwchrt_b@0 wintt_b@0 wpubt_b@0;
## RI_intt with wwchrt_b@0 wintt_b@0 wpubt_b@0;
## RI_pubtT with wwchrt_b@0 wintt_b@0 wpubt_b@0;
##
## OUTPUT: STDYX MODINDICES Tech4;
##
##
##
## INPUT READING TERMINATED NORMALLY
##
##
##
## Full Sample Model
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 4945
##
## Number of dependent variables 12
## Number of independent variables 0
## Number of continuous latent variables 15
##
## Observed dependent variables
##
## Continuous
## WCHRT_B WCHRT_1 WCHRT_2 WCHRT_3 INTT_B INTT_1
## INTT_2 INTT_3 PUBT_B PUBT_1 PUBT_2 PUBT_3
##
## Continuous latent variables
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1 WINTT_2
## WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2 WWCHRT_3 WPUBT_B
## WPUBT_1 WPUBT_2 WPUBT_3
##
##
## Estimator MLR
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Fullsample_race3_sex_subset.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 177
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.000
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## WCHRT_B WCHRT_1 WCHRT_2 WCHRT_3 INTT_B
## ________ ________ ________ ________ ________
## WCHRT_B 0.998
## WCHRT_1 0.932 0.934
## WCHRT_2 0.749 0.735 0.751
## WCHRT_3 0.168 0.166 0.149 0.168
## INTT_B 0.997 0.933 0.751 0.168 0.999
## INTT_1 0.939 0.933 0.738 0.167 0.940
## INTT_2 0.909 0.885 0.750 0.166 0.911
## INTT_3 0.839 0.818 0.697 0.167 0.840
## PUBT_B 0.721 0.675 0.549 0.135 0.722
## PUBT_1 0.393 0.390 0.376 0.097 0.393
## PUBT_2 0.837 0.816 0.691 0.151 0.838
## PUBT_3 0.795 0.775 0.663 0.158 0.797
##
##
## Covariance Coverage
## INTT_1 INTT_2 INTT_3 PUBT_B PUBT_1
## ________ ________ ________ ________ ________
## INTT_1 0.941
## INTT_2 0.891 0.911
## INTT_3 0.825 0.825 0.841
## PUBT_B 0.680 0.658 0.608 0.722
## PUBT_1 0.393 0.383 0.371 0.324 0.393
## PUBT_2 0.821 0.833 0.763 0.612 0.363
## PUBT_3 0.781 0.781 0.779 0.582 0.360
##
##
## Covariance Coverage
## PUBT_2 PUBT_3
## ________ ________
## PUBT_2 0.839
## PUBT_3 0.742 0.797
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## WCHRT_B 0.000 1.391 -2.415 0.02% -0.570 -0.295 -0.135
## 4933.000 0.511 6.535 8.909 0.02% 0.028 0.527
## WCHRT_1 0.000 2.461 -2.125 0.02% -0.604 -0.310 -0.156
## 4619.000 0.620 18.861 9.968 0.02% 0.022 0.535
## WCHRT_2 0.000 1.178 -2.197 0.03% -0.629 -0.316 -0.159
## 3714.000 0.593 2.459 5.400 0.03% 0.026 0.561
## WCHRT_3 0.000 1.793 -1.493 0.12% -0.700 -0.340 -0.195
## 833.000 0.740 8.350 7.699 0.12% 0.013 0.633
## INTT_B 0.000 2.649 -0.957 0.65% -0.936 -0.919 -0.910
## 4940.000 2.589 8.798 12.062 0.02% 0.047 1.051
## INTT_1 0.000 2.539 -1.190 0.02% -1.081 -1.008 -0.979
## 4651.000 2.957 8.235 12.912 0.02% -0.103 0.926
## INTT_2 0.000 2.396 -1.697 0.02% -1.274 -1.101 -1.005
## 4506.000 3.880 7.376 15.034 0.02% -0.293 0.861
## INTT_3 0.000 2.164 -1.822 0.05% -1.562 -1.425 -0.672
## 4157.000 5.289 5.509 13.575 0.02% -0.480 1.383
## PUBT_B 0.000 -0.158 -1.606 0.20% -0.981 -0.236 0.048
## 3571.000 0.743 -0.973 2.934 0.03% 0.480 0.792
## PUBT_1 0.000 -0.349 -2.124 0.10% -0.766 -0.044 0.115
## 1944.000 0.706 -0.284 2.830 0.05% 0.274 0.711
## PUBT_2 0.000 -0.735 -2.839 0.02% -0.533 -0.074 0.079
## 4149.000 0.574 0.946 2.308 0.02% 0.237 0.620
## PUBT_3 0.000 -1.011 -2.940 0.03% -0.528 0.032 0.142
## 3942.000 0.429 2.273 1.664 0.03% 0.252 0.472
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 69
##
## Loglikelihood
##
## H0 Value -60645.783
## H0 Scaling Correction Factor 2.5762
## for MLR
## H1 Value -60581.827
## H1 Scaling Correction Factor 2.2701
## for MLR
##
## Information Criteria
##
## Akaike (AIC) 121429.565
## Bayesian (BIC) 121878.488
## Sample-Size Adjusted BIC 121659.231
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 101.154*
## Degrees of Freedom 21
## P-Value 0.0000
## Scaling Correction Factor 1.2645
## for MLR
##
## * The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
## for chi-square difference testing in the regular way. MLM, MLR and WLSM
## chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
## and ULSMV difference testing is done using the DIFFTEST option.
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.028
## 90 Percent C.I. 0.022 0.033
## Probability RMSEA <= .05 1.000
##
## CFI/TLI
##
## CFI 0.989
## TLI 0.967
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 7651.364
## Degrees of Freedom 66
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.019
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_INTT BY
## INTT_B 1.000 0.000 999.000 999.000
## INTT_1 1.000 0.000 999.000 999.000
## INTT_2 1.000 0.000 999.000 999.000
## INTT_3 1.000 0.000 999.000 999.000
##
## RI_PUBTT BY
## PUBT_B 1.000 0.000 999.000 999.000
## PUBT_1 1.000 0.000 999.000 999.000
## PUBT_2 1.000 0.000 999.000 999.000
## PUBT_3 1.000 0.000 999.000 999.000
##
## RI_WCHRT BY
## WCHRT_B 1.000 0.000 999.000 999.000
## WCHRT_1 1.000 0.000 999.000 999.000
## WCHRT_2 1.000 0.000 999.000 999.000
## WCHRT_3 1.000 0.000 999.000 999.000
##
## WINTT_B BY
## INTT_B 1.000 0.000 999.000 999.000
##
## WINTT_1 BY
## INTT_1 1.000 0.000 999.000 999.000
##
## WINTT_2 BY
## INTT_2 1.000 0.000 999.000 999.000
##
## WINTT_3 BY
## INTT_3 1.000 0.000 999.000 999.000
##
## WWCHRT_B BY
## WCHRT_B 1.000 0.000 999.000 999.000
##
## WWCHRT_1 BY
## WCHRT_1 1.000 0.000 999.000 999.000
##
## WWCHRT_2 BY
## WCHRT_2 1.000 0.000 999.000 999.000
##
## WWCHRT_3 BY
## WCHRT_3 1.000 0.000 999.000 999.000
##
## WPUBT_B BY
## PUBT_B 1.000 0.000 999.000 999.000
##
## WPUBT_1 BY
## PUBT_1 1.000 0.000 999.000 999.000
##
## WPUBT_2 BY
## PUBT_2 1.000 0.000 999.000 999.000
##
## WPUBT_3 BY
## PUBT_3 1.000 0.000 999.000 999.000
##
## WWCHRT_1 ON
## WWCHRT_B -0.049 0.079 -0.627 0.530
## WINTT_B -0.039 0.021 -1.860 0.063
## WPUBT_B 0.061 0.018 3.348 0.001
##
## WWCHRT_2 ON
## WWCHRT_1 0.125 0.041 3.088 0.002
## WINTT_1 0.002 0.015 0.153 0.879
## WPUBT_1 0.097 0.026 3.778 0.000
##
## WWCHRT_3 ON
## WWCHRT_2 0.465 0.139 3.353 0.001
## WINTT_2 0.018 0.015 1.194 0.232
## WPUBT_2 0.056 0.036 1.547 0.122
##
## WPUBT_1 ON
## WPUBT_B 0.345 0.029 11.838 0.000
## WINTT_B 0.043 0.035 1.221 0.222
## WWCHRT_B 0.070 0.109 0.641 0.521
##
## WPUBT_2 ON
## WPUBT_1 0.422 0.028 15.131 0.000
## WINTT_1 -0.022 0.015 -1.501 0.133
## WWCHRT_1 -0.037 0.025 -1.456 0.145
##
## WPUBT_3 ON
## WPUBT_2 0.354 0.028 12.758 0.000
## WINTT_2 0.014 0.007 1.959 0.050
## WWCHRT_2 0.028 0.025 1.084 0.278
##
## WINTT_1 ON
## WINTT_B 0.027 0.085 0.319 0.750
## WPUBT_B 0.107 0.043 2.510 0.012
## WWCHRT_B 0.009 0.153 0.057 0.955
##
## WINTT_2 ON
## WINTT_1 0.257 0.056 4.563 0.000
## WPUBT_1 0.263 0.063 4.167 0.000
## WWCHRT_1 -0.156 0.060 -2.618 0.009
##
## WINTT_3 ON
## WINTT_2 0.504 0.038 13.433 0.000
## WPUBT_2 0.203 0.050 4.054 0.000
## WWCHRT_2 0.137 0.093 1.480 0.139
##
## RI_INTT WITH
## RI_PUBTT 0.006 0.019 0.337 0.736
## RI_WCHRT 0.128 0.021 5.942 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## RI_PUBTT WITH
## RI_WCHRT 0.071 0.008 8.666 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## WINTT_B WITH
## WPUBT_B 0.057 0.025 2.298 0.022
## WWCHRT_B -0.009 0.014 -0.663 0.507
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WWCHRT_B WITH
## WPUBT_B 0.031 0.010 2.993 0.003
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WINTT_1 WITH
## WPUBT_1 0.097 0.031 3.119 0.002
## WWCHRT_1 -0.020 0.022 -0.913 0.361
##
## WPUBT_1 WITH
## WWCHRT_1 0.026 0.014 1.855 0.064
##
## WINTT_2 WITH
## WPUBT_2 0.056 0.018 3.184 0.001
## WWCHRT_2 0.016 0.015 1.065 0.287
##
## WPUBT_2 WITH
## WWCHRT_2 0.003 0.006 0.438 0.661
##
## WINTT_3 WITH
## WPUBT_3 0.063 0.016 4.040 0.000
## WWCHRT_3 0.048 0.034 1.408 0.159
##
## WPUBT_3 WITH
## WWCHRT_3 0.011 0.010 1.031 0.303
##
## RI_WCHRT WITH
## WPUBT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## WCHRT_B 0.000 0.010 0.025 0.980
## WCHRT_1 0.004 0.011 0.383 0.701
## WCHRT_2 0.019 0.012 1.571 0.116
## WCHRT_3 -0.084 0.020 -4.274 0.000
## INTT_B 0.001 0.023 0.036 0.971
## INTT_1 0.007 0.025 0.287 0.774
## INTT_2 0.011 0.029 0.362 0.717
## INTT_3 0.043 0.035 1.226 0.220
## PUBT_B 0.002 0.014 0.125 0.901
## PUBT_1 0.059 0.017 3.477 0.001
## PUBT_2 0.010 0.012 0.901 0.367
## PUBT_3 0.007 0.010 0.712 0.477
##
## Variances
## RI_INTT 1.713 0.101 16.927 0.000
## RI_PUBTT 0.118 0.016 7.492 0.000
## RI_WCHRT 0.388 0.014 27.981 0.000
## WINTT_B 0.927 0.088 10.504 0.000
## WWCHRT_B 0.124 0.021 5.867 0.000
## WPUBT_B 0.627 0.018 34.374 0.000
##
## Residual Variances
## WCHRT_B 0.000 0.000 999.000 999.000
## WCHRT_1 0.000 0.000 999.000 999.000
## WCHRT_2 0.000 0.000 999.000 999.000
## WCHRT_3 0.000 0.000 999.000 999.000
## INTT_B 0.000 0.000 999.000 999.000
## INTT_1 0.000 0.000 999.000 999.000
## INTT_2 0.000 0.000 999.000 999.000
## INTT_3 0.000 0.000 999.000 999.000
## PUBT_B 0.000 0.000 999.000 999.000
## PUBT_1 0.000 0.000 999.000 999.000
## PUBT_2 0.000 0.000 999.000 999.000
## PUBT_3 0.000 0.000 999.000 999.000
## WINTT_1 1.182 0.117 10.093 0.000
## WINTT_2 2.104 0.122 17.294 0.000
## WINTT_3 3.013 0.137 21.981 0.000
## WWCHRT_1 0.223 0.042 5.334 0.000
## WWCHRT_2 0.200 0.011 18.117 0.000
## WWCHRT_3 0.273 0.055 4.952 0.000
## WPUBT_1 0.498 0.019 25.563 0.000
## WPUBT_2 0.353 0.012 30.130 0.000
## WPUBT_3 0.257 0.011 22.376 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.801E-03
## (ratio of smallest to largest eigenvalue)
##
##
## STANDARDIZED MODEL RESULTS
##
##
## STDYX Standardization
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_INTT BY
## INTT_B 0.806 0.017 48.056 0.000
## INTT_1 0.768 0.021 36.569 0.000
## INTT_2 0.658 0.016 41.947 0.000
## INTT_3 0.566 0.015 37.839 0.000
##
## RI_PUBTT BY
## PUBT_B 0.397 0.026 15.237 0.000
## PUBT_1 0.411 0.029 14.232 0.000
## PUBT_2 0.454 0.031 14.680 0.000
## PUBT_3 0.521 0.035 15.046 0.000
##
## RI_WCHRT BY
## WCHRT_B 0.871 0.019 45.088 0.000
## WCHRT_1 0.795 0.028 28.328 0.000
## WCHRT_2 0.806 0.009 88.532 0.000
## WCHRT_3 0.739 0.039 19.056 0.000
##
## WINTT_B BY
## INTT_B 0.593 0.023 26.008 0.000
##
## WINTT_1 BY
## INTT_1 0.640 0.025 25.420 0.000
##
## WINTT_2 BY
## INTT_2 0.753 0.014 54.944 0.000
##
## WINTT_3 BY
## INTT_3 0.825 0.010 80.351 0.000
##
## WWCHRT_B BY
## WCHRT_B 0.492 0.034 14.365 0.000
##
## WWCHRT_1 BY
## WCHRT_1 0.607 0.037 16.521 0.000
##
## WWCHRT_2 BY
## WCHRT_2 0.593 0.012 47.907 0.000
##
## WWCHRT_3 BY
## WCHRT_3 0.674 0.043 15.847 0.000
##
## WPUBT_B BY
## PUBT_B 0.918 0.011 81.327 0.000
##
## WPUBT_1 BY
## PUBT_1 0.912 0.013 70.071 0.000
##
## WPUBT_2 BY
## PUBT_2 0.891 0.016 56.677 0.000
##
## WPUBT_3 BY
## PUBT_3 0.854 0.021 40.444 0.000
##
## WWCHRT_1 ON
## WWCHRT_B -0.036 0.058 -0.630 0.528
## WINTT_B -0.078 0.042 -1.850 0.064
## WPUBT_B 0.102 0.033 3.073 0.002
##
## WWCHRT_2 ON
## WWCHRT_1 0.130 0.040 3.263 0.001
## WINTT_1 0.005 0.035 0.153 0.879
## WPUBT_1 0.161 0.042 3.837 0.000
##
## WWCHRT_3 ON
## WWCHRT_2 0.375 0.084 4.475 0.000
## WINTT_2 0.047 0.042 1.133 0.257
## WPUBT_2 0.066 0.042 1.569 0.117
##
## WPUBT_1 ON
## WPUBT_B 0.359 0.028 12.872 0.000
## WINTT_B 0.054 0.044 1.228 0.219
## WWCHRT_B 0.032 0.050 0.643 0.520
##
## WPUBT_2 ON
## WPUBT_1 0.477 0.029 16.552 0.000
## WINTT_1 -0.036 0.024 -1.521 0.128
## WWCHRT_1 -0.026 0.018 -1.462 0.144
##
## WPUBT_3 ON
## WPUBT_2 0.424 0.030 14.294 0.000
## WINTT_2 0.036 0.019 1.949 0.051
## WWCHRT_2 0.022 0.021 1.076 0.282
##
## WINTT_1 ON
## WINTT_B 0.024 0.075 0.320 0.749
## WPUBT_B 0.078 0.031 2.499 0.012
## WWCHRT_B 0.003 0.049 0.057 0.955
##
## WINTT_2 ON
## WINTT_1 0.187 0.044 4.259 0.000
## WPUBT_1 0.134 0.033 4.087 0.000
## WWCHRT_1 -0.049 0.018 -2.698 0.007
##
## WINTT_3 ON
## WINTT_2 0.396 0.028 14.306 0.000
## WPUBT_2 0.072 0.018 4.092 0.000
## WWCHRT_2 0.033 0.022 1.483 0.138
##
## RI_INTT WITH
## RI_PUBTT 0.014 0.043 0.339 0.735
## RI_WCHRT 0.157 0.025 6.268 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## RI_PUBTT WITH
## RI_WCHRT 0.331 0.038 8.641 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## WINTT_B WITH
## WPUBT_B 0.075 0.032 2.323 0.020
## WWCHRT_B -0.026 0.040 -0.660 0.509
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WWCHRT_B WITH
## WPUBT_B 0.111 0.035 3.155 0.002
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WINTT_1 WITH
## WPUBT_1 0.126 0.041 3.094 0.002
## WWCHRT_1 -0.040 0.044 -0.900 0.368
##
## WPUBT_1 WITH
## WWCHRT_1 0.078 0.042 1.857 0.063
##
## WINTT_2 WITH
## WPUBT_2 0.065 0.020 3.268 0.001
## WWCHRT_2 0.024 0.023 1.071 0.284
##
## WPUBT_2 WITH
## WWCHRT_2 0.010 0.022 0.438 0.661
##
## WINTT_3 WITH
## WPUBT_3 0.072 0.018 4.058 0.000
## WWCHRT_3 0.053 0.039 1.348 0.178
##
## WPUBT_3 WITH
## WWCHRT_3 0.040 0.040 1.016 0.310
##
## RI_WCHRT WITH
## WPUBT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## WCHRT_B 0.000 0.014 0.025 0.980
## WCHRT_1 0.006 0.014 0.386 0.699
## WCHRT_2 0.024 0.015 1.594 0.111
## WCHRT_3 -0.099 0.025 -3.899 0.000
## INTT_B 0.001 0.014 0.036 0.971
## INTT_1 0.004 0.015 0.289 0.773
## INTT_2 0.005 0.015 0.365 0.715
## INTT_3 0.019 0.015 1.250 0.211
## PUBT_B 0.002 0.016 0.125 0.901
## PUBT_1 0.070 0.020 3.439 0.001
## PUBT_2 0.014 0.015 0.897 0.370
## PUBT_3 0.011 0.016 0.708 0.479
##
## Variances
## RI_INTT 1.000 0.000 999.000 999.000
## RI_PUBTT 1.000 0.000 999.000 999.000
## RI_WCHRT 1.000 0.000 999.000 999.000
## WINTT_B 1.000 0.000 999.000 999.000
## WWCHRT_B 1.000 0.000 999.000 999.000
## WPUBT_B 1.000 0.000 999.000 999.000
##
## Residual Variances
## WCHRT_B 0.000 999.000 999.000 999.000
## WCHRT_1 0.000 999.000 999.000 999.000
## WCHRT_2 0.000 999.000 999.000 999.000
## WCHRT_3 0.000 999.000 999.000 999.000
## INTT_B 0.000 999.000 999.000 999.000
## INTT_1 0.000 999.000 999.000 999.000
## INTT_2 0.000 999.000 999.000 999.000
## INTT_3 0.000 999.000 999.000 999.000
## PUBT_B 0.000 999.000 999.000 999.000
## PUBT_1 0.000 999.000 999.000 999.000
## PUBT_2 0.000 999.000 999.000 999.000
## PUBT_3 0.000 999.000 999.000 999.000
## WINTT_1 0.993 0.006 155.392 0.000
## WINTT_2 0.938 0.019 48.952 0.000
## WINTT_3 0.828 0.023 36.709 0.000
## WWCHRT_1 0.984 0.010 101.885 0.000
## WWCHRT_2 0.953 0.019 51.456 0.000
## WWCHRT_3 0.846 0.062 13.563 0.000
## WPUBT_1 0.861 0.023 37.589 0.000
## WPUBT_2 0.778 0.026 29.559 0.000
## WPUBT_3 0.813 0.025 31.987 0.000
##
##
## R-SQUARE
##
## Observed Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WCHRT_B 1.000 999.000 999.000 999.000
## WCHRT_1 1.000 999.000 999.000 999.000
## WCHRT_2 1.000 999.000 999.000 999.000
## WCHRT_3 1.000 999.000 999.000 999.000
## INTT_B 1.000 999.000 999.000 999.000
## INTT_1 1.000 999.000 999.000 999.000
## INTT_2 1.000 999.000 999.000 999.000
## INTT_3 1.000 999.000 999.000 999.000
## PUBT_B 1.000 999.000 999.000 999.000
## PUBT_1 1.000 999.000 999.000 999.000
## PUBT_2 1.000 999.000 999.000 999.000
## PUBT_3 1.000 999.000 999.000 999.000
##
## Latent Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WINTT_1 0.007 0.006 1.084 0.278
## WINTT_2 0.062 0.019 3.236 0.001
## WINTT_3 0.172 0.023 7.601 0.000
## WWCHRT_1 0.016 0.010 1.619 0.105
## WWCHRT_2 0.047 0.019 2.549 0.011
## WWCHRT_3 0.154 0.062 2.463 0.014
## WPUBT_1 0.139 0.023 6.044 0.000
## WPUBT_2 0.222 0.026 8.416 0.000
## WPUBT_3 0.187 0.025 7.376 0.000
##
##
## MODEL MODIFICATION INDICES
##
## NOTE: Modification indices for direct effects of observed dependent variables
## regressed on covariates may not be included. To include these, request
## MODINDICES (ALL).
##
## Minimum M.I. value for printing the modification index 10.000
##
## M.I. E.P.C. Std E.P.C. StdYX E.P.C.
##
## BY Statements
##
## RI_INTT BY INTT_B 35.388 -0.330 -0.432 -0.266
## RI_INTT BY INTT_1 33.662 0.257 0.336 0.197
## RI_PUBTT BY PUBT_3 14.493 -0.460 -0.158 -0.240
## RI_WCHRT BY PUBT_3 16.809 -0.103 -0.064 -0.097
## WINTT_B BY INTT_3 16.609 -0.187 -0.180 -0.078
## WINTT_1 BY INTT_2 37.330 -0.446 -0.486 -0.244
## WINTT_1 BY INTT_3 37.983 0.227 0.248 0.107
## WINTT_3 BY INTT_B 24.241 -0.075 -0.144 -0.088
## WINTT_3 BY INTT_1 36.756 0.083 0.159 0.093
## WINTT_3 BY INTT_2 32.328 -0.484 -0.924 -0.464
## WPUBT_B BY PUBT_3 12.730 -0.092 -0.073 -0.110
## WPUBT_1 BY PUBT_2 16.209 -0.304 -0.231 -0.306
## WPUBT_1 BY PUBT_3 14.845 0.105 0.080 0.122
## WPUBT_3 BY PUBT_B 14.009 -0.224 -0.126 -0.146
## WPUBT_3 BY PUBT_1 18.720 0.163 0.092 0.110
## WPUBT_3 BY PUBT_2 16.726 -0.375 -0.211 -0.279
##
## ON/BY Statements
##
## RI_INTT ON WINTT_B /
## WINTT_B BY RI_INTT 31.764 -0.590 -0.434 -0.434
## RI_INTT ON WINTT_1 /
## WINTT_1 BY RI_INTT 37.863 0.453 0.378 0.378
## RI_PUBTT ON WPUBT_B /
## WPUBT_B BY RI_PUBTT 21.805 -0.331 -0.765 -0.765
## RI_PUBTT ON WPUBT_1 /
## WPUBT_1 BY RI_PUBTT 13.462 0.154 0.341 0.341
## RI_WCHRT ON WPUBT_B /
## WPUBT_B BY RI_WCHRT 10.372 0.079 0.100 0.100
## RI_WCHRT ON WPUBT_3 /
## WPUBT_3 BY RI_WCHRT 14.447 -0.187 -0.169 -0.169
## WINTT_B ON RI_INTT /
## RI_INTT BY WINTT_B 35.625 -0.334 -0.455 -0.455
## WINTT_B ON WINTT_3 /
## WINTT_3 BY WINTT_B 23.047 -0.074 -0.147 -0.147
## WINTT_1 ON RI_INTT /
## RI_INTT BY WINTT_1 36.215 0.309 0.370 0.370
## WINTT_1 ON WINTT_3 /
## WINTT_3 BY WINTT_1 36.743 0.087 0.151 0.151
## WINTT_2 ON WINTT_3 /
## WINTT_3 BY WINTT_2 32.329 -0.484 -0.617 -0.617
## WINTT_3 ON WINTT_B /
## WINTT_B BY WINTT_3 16.609 -0.187 -0.094 -0.094
## WINTT_3 ON WINTT_1 /
## WINTT_1 BY WINTT_3 37.983 0.227 0.130 0.130
## WPUBT_B ON RI_WCHRT /
## RI_WCHRT BY WPUBT_B 11.141 0.133 0.105 0.105
## WPUBT_B ON WWCHRT_3 /
## WWCHRT_3 BY WPUBT_B 10.932 0.236 0.169 0.169
## WPUBT_1 ON WPUBT_3 /
## WPUBT_3 BY WPUBT_1 19.351 0.206 0.153 0.153
## WPUBT_2 ON WPUBT_3 /
## WPUBT_3 BY WPUBT_2 16.724 -0.375 -0.313 -0.313
## WPUBT_3 ON RI_PUBTT /
## RI_PUBTT BY WPUBT_3 14.493 -0.460 -0.281 -0.281
## WPUBT_3 ON RI_WCHRT /
## RI_WCHRT BY WPUBT_3 16.809 -0.103 -0.114 -0.114
## WPUBT_3 ON WPUBT_B /
## WPUBT_B BY WPUBT_3 12.730 -0.092 -0.129 -0.129
## WPUBT_3 ON WPUBT_1 /
## WPUBT_1 BY WPUBT_3 14.845 0.105 0.143 0.143
##
## WITH Statements
##
## WCHRT_3 WITH WCHRT_B 11.445 -0.040 -0.040 999.000
## INTT_2 WITH INTT_1 38.169 -0.507 -0.507 999.000
## INTT_3 WITH INTT_B 17.891 -0.178 -0.178 999.000
## INTT_3 WITH INTT_1 37.761 0.255 0.255 999.000
## INTT_3 WITH INTT_2 31.771 -1.445 -1.445 999.000
## PUBT_2 WITH PUBT_1 26.144 -0.111 -0.111 999.000
## PUBT_3 WITH PUBT_B 17.404 -0.051 -0.051 999.000
## PUBT_3 WITH PUBT_1 22.727 0.047 0.047 999.000
## PUBT_3 WITH PUBT_2 16.656 -0.096 -0.096 999.000
## WINTT_B WITH RI_INTT 35.921 -0.585 -0.464 -0.464
## WINTT_1 WITH RI_INTT 36.663 0.520 0.365 0.365
## WINTT_3 WITH WINTT_B 16.893 -0.174 -0.104 -0.104
## WINTT_3 WITH WINTT_1 37.769 0.265 0.140 0.140
## WINTT_3 WITH WINTT_2 31.763 -1.445 -0.574 -0.574
## WWCHRT_3 WITH WWCHRT_B 11.609 -0.039 -0.210 -0.210
## WPUBT_B WITH RI_PUBTT 21.174 -0.191 -0.705 -0.705
## WPUBT_B WITH RI_WCHRT 10.944 0.051 0.103 0.103
## WPUBT_1 WITH RI_PUBTT 17.634 0.074 0.306 0.306
## WPUBT_3 WITH RI_WCHRT 13.915 -0.036 -0.113 -0.113
## WPUBT_3 WITH WPUBT_B 11.571 -0.053 -0.133 -0.133
## WPUBT_3 WITH WPUBT_1 24.324 0.060 0.169 0.169
## WPUBT_3 WITH WPUBT_2 16.654 -0.096 -0.320 -0.320
##
## Variances/Residual Variances
##
## INTT_2 32.214 2.885 2.885 0.729
## PUBT_2 16.330 0.263 0.263 0.461
##
##
## TECHNICAL 4 OUTPUT
##
##
## ESTIMATES DERIVED FROM THE MODEL
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 1.713
## RI_PUBTT 0.006 0.118
## RI_WCHRT 0.128 0.071 0.388
## WINTT_B 0.000 0.000 0.000 0.927
## WINTT_1 0.000 0.000 0.000 0.031 1.191
## WINTT_2 0.000 0.000 0.000 0.028 0.341
## WINTT_3 0.000 0.000 0.000 0.020 0.179
## WWCHRT_B 0.000 0.000 0.000 -0.009 0.004
## WWCHRT_1 0.000 0.000 0.000 -0.032 -0.018
## WWCHRT_2 0.000 0.000 0.000 0.002 0.012
## WWCHRT_3 0.000 0.000 0.000 0.003 0.013
## WPUBT_B 0.000 0.000 0.000 0.057 0.069
## WPUBT_1 0.000 0.000 0.000 0.059 0.122
## WPUBT_2 0.000 0.000 0.000 0.025 0.025
## WPUBT_3 0.000 0.000 0.000 0.009 0.014
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 2.243
## WINTT_3 1.160 3.637
## WWCHRT_B 0.007 0.005 0.124
## WWCHRT_1 -0.030 -0.009 -0.004 0.226
## WWCHRT_2 0.030 0.049 0.001 0.032 0.210
## WWCHRT_3 0.061 0.100 0.001 0.015 0.100
## WPUBT_B 0.071 0.057 0.031 0.035 0.026
## WPUBT_1 0.178 0.147 0.019 0.036 0.061
## WPUBT_2 0.125 0.159 0.008 0.007 0.027
## WPUBT_3 0.076 0.137 0.003 0.003 0.016
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.323
## WPUBT_B 0.018 0.627
## WPUBT_1 0.045 0.221 0.579
## WPUBT_2 0.040 0.091 0.240 0.454
## WPUBT_3 0.028 0.034 0.089 0.163 0.316
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.101
## RI_PUBTT 0.019 0.016
## RI_WCHRT 0.021 0.008 0.014
## WINTT_B 0.000 0.000 0.000 0.088
## WINTT_1 0.000 0.000 0.000 0.080 0.122
## WINTT_2 0.000 0.000 0.000 0.026 0.088
## WINTT_3 0.000 0.000 0.000 0.016 0.052
## WWCHRT_B 0.000 0.000 0.000 0.014 0.019
## WWCHRT_1 0.000 0.000 0.000 0.019 0.022
## WWCHRT_2 0.000 0.000 0.000 0.005 0.020
## WWCHRT_3 0.000 0.000 0.000 0.003 0.011
## WPUBT_B 0.000 0.000 0.000 0.025 0.028
## WPUBT_1 0.000 0.000 0.000 0.036 0.036
## WPUBT_2 0.000 0.000 0.000 0.015 0.024
## WPUBT_3 0.000 0.000 0.000 0.006 0.010
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.138
## WINTT_3 0.119 0.187
## WWCHRT_B 0.007 0.005 0.021
## WWCHRT_1 0.017 0.010 0.009 0.041
## WWCHRT_2 0.018 0.023 0.002 0.010 0.012
## WWCHRT_3 0.034 0.040 0.002 0.007 0.029
## WPUBT_B 0.020 0.014 0.010 0.011 0.007
## WPUBT_1 0.042 0.029 0.016 0.016 0.017
## WPUBT_2 0.025 0.030 0.007 0.009 0.010
## WPUBT_3 0.020 0.023 0.003 0.004 0.007
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.073
## WPUBT_B 0.007 0.018
## WPUBT_1 0.015 0.023 0.027
## WPUBT_2 0.018 0.014 0.023 0.022
## WPUBT_3 0.013 0.007 0.014 0.018 0.020
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 16.927
## RI_PUBTT 0.337 7.492
## RI_WCHRT 5.942 8.666 27.981
## WINTT_B 0.000 0.000 0.000 10.504
## WINTT_1 0.000 0.000 0.000 0.388 9.783
## WINTT_2 0.000 0.000 0.000 1.104 3.859
## WINTT_3 0.000 0.000 0.000 1.270 3.440
## WWCHRT_B 0.000 0.000 0.000 -0.663 0.214
## WWCHRT_1 0.000 0.000 0.000 -1.722 -0.795
## WWCHRT_2 0.000 0.000 0.000 0.336 0.622
## WWCHRT_3 0.000 0.000 0.000 0.784 1.154
## WPUBT_B 0.000 0.000 0.000 2.298 2.422
## WPUBT_1 0.000 0.000 0.000 1.624 3.363
## WPUBT_2 0.000 0.000 0.000 1.638 1.063
## WPUBT_3 0.000 0.000 0.000 1.590 1.443
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 16.222
## WINTT_3 9.758 19.456
## WWCHRT_B 0.933 1.061 5.867
## WWCHRT_1 -1.783 -0.908 -0.413 5.478
## WWCHRT_2 1.640 2.137 0.562 3.277 17.173
## WWCHRT_3 1.802 2.487 0.733 1.982 3.392
## WPUBT_B 3.542 4.113 2.993 3.021 3.529
## WPUBT_1 4.224 4.983 1.215 2.300 3.642
## WPUBT_2 5.089 5.360 1.219 0.832 2.797
## WPUBT_3 3.776 5.987 1.185 0.865 2.173
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 4.400
## WPUBT_B 2.818 34.374
## WPUBT_1 2.981 9.656 21.331
## WPUBT_2 2.264 6.688 10.400 20.745
## WPUBT_3 2.238 4.852 6.375 8.814 16.071
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.736 0.000
## RI_WCHRT 0.000 0.000 0.000
## WINTT_B 1.000 1.000 1.000 0.000
## WINTT_1 1.000 1.000 1.000 0.698 0.000
## WINTT_2 1.000 1.000 1.000 0.269 0.000
## WINTT_3 1.000 1.000 1.000 0.204 0.001
## WWCHRT_B 1.000 1.000 1.000 0.507 0.831
## WWCHRT_1 1.000 1.000 1.000 0.085 0.426
## WWCHRT_2 1.000 1.000 1.000 0.737 0.534
## WWCHRT_3 1.000 1.000 1.000 0.433 0.249
## WPUBT_B 1.000 1.000 1.000 0.022 0.015
## WPUBT_1 1.000 1.000 1.000 0.104 0.001
## WPUBT_2 1.000 1.000 1.000 0.101 0.288
## WPUBT_3 1.000 1.000 1.000 0.112 0.149
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.000 0.000
## WWCHRT_B 0.351 0.289 0.000
## WWCHRT_1 0.075 0.364 0.680 0.000
## WWCHRT_2 0.101 0.033 0.574 0.001 0.000
## WWCHRT_3 0.072 0.013 0.463 0.048 0.001
## WPUBT_B 0.000 0.000 0.003 0.003 0.000
## WPUBT_1 0.000 0.000 0.224 0.021 0.000
## WPUBT_2 0.000 0.000 0.223 0.405 0.005
## WPUBT_3 0.000 0.000 0.236 0.387 0.030
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.005 0.000
## WPUBT_1 0.003 0.000 0.000
## WPUBT_2 0.024 0.000 0.000 0.000
## WPUBT_3 0.025 0.000 0.000 0.000 0.000
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 1.000
## RI_PUBTT 0.014 1.000
## RI_WCHRT 0.157 0.331 1.000
## WINTT_B 0.000 0.000 0.000 1.000
## WINTT_1 0.000 0.000 0.000 0.030 1.000
## WINTT_2 0.000 0.000 0.000 0.020 0.208
## WINTT_3 0.000 0.000 0.000 0.011 0.086
## WWCHRT_B 0.000 0.000 0.000 -0.026 0.011
## WWCHRT_1 0.000 0.000 0.000 -0.070 -0.034
## WWCHRT_2 0.000 0.000 0.000 0.004 0.025
## WWCHRT_3 0.000 0.000 0.000 0.005 0.021
## WPUBT_B 0.000 0.000 0.000 0.075 0.080
## WPUBT_1 0.000 0.000 0.000 0.080 0.147
## WPUBT_2 0.000 0.000 0.000 0.039 0.035
## WPUBT_3 0.000 0.000 0.000 0.017 0.023
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 1.000
## WINTT_3 0.406 1.000
## WWCHRT_B 0.013 0.008 1.000
## WWCHRT_1 -0.042 -0.010 -0.023 1.000
## WWCHRT_2 0.044 0.057 0.008 0.146 1.000
## WWCHRT_3 0.072 0.093 0.006 0.054 0.383
## WPUBT_B 0.059 0.038 0.111 0.092 0.071
## WPUBT_1 0.156 0.101 0.071 0.100 0.175
## WPUBT_2 0.124 0.124 0.034 0.023 0.087
## WPUBT_3 0.090 0.127 0.015 0.012 0.061
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 1.000
## WPUBT_B 0.041 1.000
## WPUBT_1 0.104 0.367 1.000
## WPUBT_2 0.104 0.170 0.469 1.000
## WPUBT_3 0.089 0.076 0.209 0.431 1.000
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.043 0.000
## RI_WCHRT 0.025 0.038 0.000
## WINTT_B 0.000 0.000 0.000 0.000
## WINTT_1 0.000 0.000 0.000 0.074 0.000
## WINTT_2 0.000 0.000 0.000 0.017 0.043
## WINTT_3 0.000 0.000 0.000 0.008 0.021
## WWCHRT_B 0.000 0.000 0.000 0.040 0.050
## WWCHRT_1 0.000 0.000 0.000 0.041 0.043
## WWCHRT_2 0.000 0.000 0.000 0.012 0.039
## WWCHRT_3 0.000 0.000 0.000 0.006 0.018
## WPUBT_B 0.000 0.000 0.000 0.032 0.032
## WPUBT_1 0.000 0.000 0.000 0.049 0.042
## WPUBT_2 0.000 0.000 0.000 0.024 0.032
## WPUBT_3 0.000 0.000 0.000 0.011 0.016
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.028 0.000
## WWCHRT_B 0.013 0.007 0.000
## WWCHRT_1 0.023 0.011 0.058 0.000
## WWCHRT_2 0.026 0.026 0.015 0.041 0.000
## WWCHRT_3 0.042 0.042 0.008 0.023 0.083
## WPUBT_B 0.016 0.009 0.035 0.032 0.019
## WPUBT_1 0.036 0.019 0.057 0.043 0.046
## WPUBT_2 0.023 0.022 0.027 0.028 0.030
## WPUBT_3 0.023 0.021 0.012 0.013 0.027
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.013 0.000
## WPUBT_1 0.030 0.029 0.000
## WPUBT_2 0.044 0.021 0.028 0.000
## WPUBT_3 0.039 0.013 0.024 0.030 0.000
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 999.000
## RI_PUBTT 0.339 999.000
## RI_WCHRT 6.268 8.641 999.000
## WINTT_B 0.000 0.000 0.000 999.000
## WINTT_1 0.000 0.000 0.000 0.398 999.000
## WINTT_2 0.000 0.000 0.000 1.138 4.834
## WINTT_3 0.000 0.000 0.000 1.307 4.063
## WWCHRT_B 0.000 0.000 0.000 -0.660 0.215
## WWCHRT_1 0.000 0.000 0.000 -1.691 -0.789
## WWCHRT_2 0.000 0.000 0.000 0.336 0.626
## WWCHRT_3 0.000 0.000 0.000 0.785 1.155
## WPUBT_B 0.000 0.000 0.000 2.323 2.494
## WPUBT_1 0.000 0.000 0.000 1.631 3.462
## WPUBT_2 0.000 0.000 0.000 1.642 1.070
## WPUBT_3 0.000 0.000 0.000 1.602 1.466
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 999.000
## WINTT_3 14.698 999.000
## WWCHRT_B 0.937 1.069 999.000
## WWCHRT_1 -1.815 -0.914 -0.401 999.000
## WWCHRT_2 1.657 2.161 0.571 3.573 999.000
## WWCHRT_3 1.692 2.228 0.752 2.376 4.617
## WPUBT_B 3.629 4.306 3.155 2.893 3.716
## WPUBT_1 4.315 5.280 1.233 2.322 3.835
## WPUBT_2 5.343 5.559 1.234 0.831 2.889
## WPUBT_3 3.896 6.192 1.201 0.862 2.244
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 999.000
## WPUBT_B 3.240 999.000
## WPUBT_1 3.438 12.714 999.000
## WPUBT_2 2.388 8.219 16.783 999.000
## WPUBT_3 2.304 5.762 8.627 14.588 999.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.735 0.000
## RI_WCHRT 0.000 0.000 0.000
## WINTT_B 1.000 1.000 1.000 0.000
## WINTT_1 1.000 1.000 1.000 0.691 0.000
## WINTT_2 1.000 1.000 1.000 0.255 0.000
## WINTT_3 1.000 1.000 1.000 0.191 0.000
## WWCHRT_B 1.000 1.000 1.000 0.509 0.830
## WWCHRT_1 1.000 1.000 1.000 0.091 0.430
## WWCHRT_2 1.000 1.000 1.000 0.737 0.531
## WWCHRT_3 1.000 1.000 1.000 0.432 0.248
## WPUBT_B 1.000 1.000 1.000 0.020 0.013
## WPUBT_1 1.000 1.000 1.000 0.103 0.001
## WPUBT_2 1.000 1.000 1.000 0.101 0.284
## WPUBT_3 1.000 1.000 1.000 0.109 0.143
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.000 0.000
## WWCHRT_B 0.349 0.285 0.000
## WWCHRT_1 0.069 0.361 0.689 0.000
## WWCHRT_2 0.098 0.031 0.568 0.000 0.000
## WWCHRT_3 0.091 0.026 0.452 0.018 0.000
## WPUBT_B 0.000 0.000 0.002 0.004 0.000
## WPUBT_1 0.000 0.000 0.218 0.020 0.000
## WPUBT_2 0.000 0.000 0.217 0.406 0.004
## WPUBT_3 0.000 0.000 0.230 0.389 0.025
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.001 0.000
## WPUBT_1 0.001 0.000 0.000
## WPUBT_2 0.017 0.000 0.000 0.000
## WPUBT_3 0.021 0.000 0.000 0.000 0.000
##
##
## DIAGRAM INFORMATION
##
## Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram.
## If running Mplus from the Mplus Diagrammer, the diagram opens automatically.
##
## Diagram output
## c:\users\ocrobert\onedrive - indiana university\abcd\obesity week\mplus\all full model girls.dgm
##
## Beginning Time: 14:24:53
## Ending Time: 14:24:53
## Elapsed Time: 00:00:00
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2023 Muthen & Muthen
# Specify the path to your Mplus output file
output_path <- "C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Obesity Week/Mplus/black full model.out"
# Read and print the file
output_content <- readLines(output_path)
cat(output_content, sep = "\n")
## Mplus VERSION 8.10
## MUTHEN & MUTHEN
## 07/02/2024 11:58 AM
##
## INPUT INSTRUCTIONS
##
## TITLE: Full Sample Model
## DATA: FILE = "Fullsample_race3_sex_subset.dat";
## VARIABLE:
## NAMES = sex race3 age_B int_B intt_B age_1 int_1 intt_1
## age_2 int_2 intt_2 age_3 int_3 intt_3 wchr_B wchrt_B
## wchr_1 wchrt_1 wchr_2 wchrt_2 wchr_3 wchrt_3 pub_B pubt_B
## pub_1 pubt_1 pub_2 pubt_2 pub_3 pubt_3 int_p_ave famc_ave bw_lbs matage
## matalc_ave matcig_ave matmar_ave ses_lt;
## MISSING=.;
##
## USEVARIABLES ARE
## wchrt_b wchrt_1
## wchrt_2 wchrt_3
## intt_b intt_1
## intt_2 intt_3
## pubt_b pubt_1
## pubt_2 pubt_3;
##
## USEOBSERVATIONS ARE (race3 EQ 2);
##
## DEFINE:
## wchrt_b = wchrt_b*10;
## wchrt_1 = wchrt_1*10;
## wchrt_2 = wchrt_2*10;
## wchrt_3 = wchrt_3*10;
##
## ANALYSIS:
## ESTIMATOR=MLR;
## MODEL=NOCOVARIANCES;
## COVERAGE = 0;
##
## MODEL:
## ! Estimate random inttercept)
## RI_intt BY intt_b@1 intt_1@1 intt_2@1 intt_3@1;
## RI_pubtT BY pubt_b@1 pubt_1@1 pubt_2@1 pubt_3@1;
## RI_wchrt BY wchrt_b@1 wchrt_1@1 wchrt_2@1 wchrt_3@1;
##
## ! RI correlations
## RI_intt with RI_pubtT RI_wchrt;
## RI_pubtT with RI_wchrt;
##
## ! Create within-person centered variables
## wintt_b BY intt_b@1;
## wintt_1 BY intt_1@1;
## wintt_2 BY intt_2@1;
## wintt_3 BY intt_3@1;
##
## wwchrt_b BY wchrt_b@1;
## wwchrt_1 BY wchrt_1@1;
## wwchrt_2 BY wchrt_2@1;
## wwchrt_3 BY wchrt_3@1;
##
## wpubt_b BY pubt_b@1;
## wpubt_1 BY pubt_1@1;
## wpubt_2 BY pubt_2@1;
## wpubt_3 BY pubt_3@1;
##
## ! Constrain the measurement error variances to zero
## intt_b@0;
## intt_1@0;
## intt_2@0;
## intt_3@0;
##
## wchrt_b@0;
## wchrt_1@0;
## wchrt_2@0;
## wchrt_3@0;
##
## pubt_b@0;
## pubt_1@0;
## pubt_2@0;
## pubt_3@0;
##
## ! Estimate the Lagged Effects
## wwchrt_1 ON wwchrt_b;
## wwchrt_1 ON wintt_b;
## wwchrt_1 ON wpubt_b;
##
## wwchrt_2 ON wwchrt_1;
## wwchrt_2 ON wintt_1;
## wwchrt_2 ON wpubt_1;
##
## wwchrt_3 ON wwchrt_2;
## wwchrt_3 ON wintt_2;
## wwchrt_3 ON wpubt_2;
##
## wpubt_1 ON wpubt_b;
## wpubt_1 ON wintt_b;
## wpubt_1 ON wwchrt_b;
##
## wpubt_2 ON wpubt_1;
## wpubt_2 ON wintt_1;
## wpubt_2 ON wwchrt_1;
##
## wpubt_3 ON wpubt_2;
## wpubt_3 ON wintt_2;
## wpubt_3 ON wwchrt_2;
##
## wintt_1 ON wintt_b;
## wintt_1 ON wpubt_b;
## wintt_1 ON wwchrt_b;
##
## wintt_2 ON wintt_1;
## wintt_2 ON wpubt_1;
## wintt_2 ON wwchrt_1;
##
## wintt_3 ON wintt_2;
## wintt_3 ON wpubt_2;
## wintt_3 ON wwchrt_2;
##
## ! Estimate the covariance between the within-person
## ! centered variables at the first wave
## wintt_b with wpubt_b;
## wwchrt_b with wintt_b wpubt_b;
##
## ! Estimate covariances between residuals of within-person components
## ! (i.e., innovations)
## wintt_1 with wpubt_1 wwchrt_1;
## wpubt_1 with wwchrt_1;
##
## wintt_2 with wpubt_2 wwchrt_2;
## wpubt_2 with wwchrt_2;
##
## wintt_3 with wpubt_3 wwchrt_3;
## wpubt_3 with wwchrt_3;
##
## ! ask for variances for all variables that are included;
## [intt_b intt_1 intt_2 intt_3];
## [pubt_b pubt_1 pubt_2 pubt_3];
## [wchrt_b wchrt_1 wchrt_2 wchrt_3];
##
## ! Fix the correlation between the individual factors and the other
## ! exogenous variables to zero (by default these would be estimated)
## RI_wchrt with wwchrt_b@0 wintt_b@0 wpubt_b@0;
## RI_intt with wwchrt_b@0 wintt_b@0 wpubt_b@0;
## RI_pubtT with wwchrt_b@0 wintt_b@0 wpubt_b@0;
##
## OUTPUT: STDYX MODINDICES Tech4;
##
##
##
## *** WARNING
## Data set contains cases with missing on all variables.
## These cases were not included in the analysis.
## Number of cases with missing on all variables: 2
## 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
##
##
##
## Full Sample Model
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 1782
##
## Number of dependent variables 12
## Number of independent variables 0
## Number of continuous latent variables 15
##
## Observed dependent variables
##
## Continuous
## WCHRT_B WCHRT_1 WCHRT_2 WCHRT_3 INTT_B INTT_1
## INTT_2 INTT_3 PUBT_B PUBT_1 PUBT_2 PUBT_3
##
## Continuous latent variables
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1 WINTT_2
## WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2 WWCHRT_3 WPUBT_B
## WPUBT_1 WPUBT_2 WPUBT_3
##
##
## Estimator MLR
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Fullsample_race3_sex_subset.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 125
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.000
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## WCHRT_B WCHRT_1 WCHRT_2 WCHRT_3 INTT_B
## ________ ________ ________ ________ ________
## WCHRT_B 0.999
## WCHRT_1 0.878 0.879
## WCHRT_2 0.668 0.632 0.669
## WCHRT_3 0.148 0.144 0.125 0.148
## INTT_B 0.999 0.878 0.669 0.148 0.999
## INTT_1 0.893 0.878 0.640 0.145 0.893
## INTT_2 0.868 0.809 0.668 0.143 0.868
## INTT_3 0.744 0.701 0.572 0.148 0.744
## PUBT_B 0.791 0.693 0.531 0.126 0.791
## PUBT_1 0.276 0.272 0.266 0.061 0.276
## PUBT_2 0.796 0.744 0.614 0.130 0.797
## PUBT_3 0.694 0.654 0.536 0.135 0.695
##
##
## Covariance Coverage
## INTT_1 INTT_2 INTT_3 PUBT_B PUBT_1
## ________ ________ ________ ________ ________
## INTT_1 0.893
## INTT_2 0.824 0.868
## INTT_3 0.715 0.720 0.744
## PUBT_B 0.705 0.685 0.586 0.791
## PUBT_1 0.276 0.270 0.250 0.239 0.276
## PUBT_2 0.756 0.791 0.662 0.636 0.258
## PUBT_3 0.667 0.672 0.680 0.551 0.241
##
##
## Covariance Coverage
## PUBT_2 PUBT_3
## ________ ________
## PUBT_2 0.797
## PUBT_3 0.633 0.695
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## WCHRT_B 0.119 0.824 -2.057 0.06% -0.558 -0.224 -0.041
## 1780.000 0.645 0.648 4.101 0.06% 0.171 0.768
## WCHRT_1 0.145 0.978 -1.763 0.06% -0.569 -0.243 -0.044
## 1566.000 0.685 1.406 5.188 0.06% 0.211 0.817
## WCHRT_2 0.229 1.783 -1.811 0.08% -0.544 -0.188 0.012
## 1192.000 0.907 8.590 9.219 0.08% 0.288 0.939
## WCHRT_3 0.284 0.859 -1.221 0.38% -0.648 -0.207 0.067
## 264.000 1.001 0.649 4.586 0.38% 0.357 1.156
## INTT_B 0.057 2.502 -1.161 0.45% -1.092 -0.929 -0.912
## 1781.000 3.287 7.339 10.864 0.06% -0.125 0.922
## INTT_1 0.012 2.546 -1.298 0.06% -1.122 -1.038 -0.993
## 1592.000 3.263 8.416 12.777 0.06% -0.139 0.912
## INTT_2 -0.094 2.667 -1.639 0.13% -1.281 -1.133 -1.043
## 1547.000 3.590 9.684 13.660 0.06% -0.340 0.803
## INTT_3 -0.187 2.287 -1.781 0.08% -1.470 -1.339 -1.263
## 1326.000 4.272 6.001 12.465 0.08% -0.507 0.643
## PUBT_B 0.321 -0.112 -1.577 0.07% -0.322 0.067 0.437
## 1410.000 0.654 -0.149 3.081 0.07% 0.650 1.047
## PUBT_1 0.361 -0.305 -2.004 0.20% -0.069 0.142 0.274
## 491.000 0.560 0.214 2.473 0.20% 0.592 1.035
## PUBT_2 0.278 -0.372 -2.227 0.07% -0.304 0.142 0.369
## 1420.000 0.560 0.531 2.822 0.07% 0.531 0.887
## PUBT_3 0.179 -0.531 -2.858 0.08% -0.390 0.115 0.230
## 1238.000 0.495 1.235 2.306 0.08% 0.362 0.637
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 69
##
## Loglikelihood
##
## H0 Value -21047.189
## H0 Scaling Correction Factor 1.9353
## for MLR
## H1 Value -21025.684
## H1 Scaling Correction Factor 1.7309
## for MLR
##
## Information Criteria
##
## Akaike (AIC) 42232.378
## Bayesian (BIC) 42610.877
## Sample-Size Adjusted BIC 42391.669
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 40.604*
## Degrees of Freedom 21
## P-Value 0.0063
## Scaling Correction Factor 1.0593
## for MLR
##
## * The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
## for chi-square difference testing in the regular way. MLM, MLR and WLSM
## chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
## and ULSMV difference testing is done using the DIFFTEST option.
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.023
## 90 Percent C.I. 0.012 0.033
## Probability RMSEA <= .05 1.000
##
## CFI/TLI
##
## CFI 0.995
## TLI 0.983
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 3663.489
## Degrees of Freedom 66
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.024
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_INTT BY
## INTT_B 1.000 0.000 999.000 999.000
## INTT_1 1.000 0.000 999.000 999.000
## INTT_2 1.000 0.000 999.000 999.000
## INTT_3 1.000 0.000 999.000 999.000
##
## RI_PUBTT BY
## PUBT_B 1.000 0.000 999.000 999.000
## PUBT_1 1.000 0.000 999.000 999.000
## PUBT_2 1.000 0.000 999.000 999.000
## PUBT_3 1.000 0.000 999.000 999.000
##
## RI_WCHRT BY
## WCHRT_B 1.000 0.000 999.000 999.000
## WCHRT_1 1.000 0.000 999.000 999.000
## WCHRT_2 1.000 0.000 999.000 999.000
## WCHRT_3 1.000 0.000 999.000 999.000
##
## WINTT_B BY
## INTT_B 1.000 0.000 999.000 999.000
##
## WINTT_1 BY
## INTT_1 1.000 0.000 999.000 999.000
##
## WINTT_2 BY
## INTT_2 1.000 0.000 999.000 999.000
##
## WINTT_3 BY
## INTT_3 1.000 0.000 999.000 999.000
##
## WWCHRT_B BY
## WCHRT_B 1.000 0.000 999.000 999.000
##
## WWCHRT_1 BY
## WCHRT_1 1.000 0.000 999.000 999.000
##
## WWCHRT_2 BY
## WCHRT_2 1.000 0.000 999.000 999.000
##
## WWCHRT_3 BY
## WCHRT_3 1.000 0.000 999.000 999.000
##
## WPUBT_B BY
## PUBT_B 1.000 0.000 999.000 999.000
##
## WPUBT_1 BY
## PUBT_1 1.000 0.000 999.000 999.000
##
## WPUBT_2 BY
## PUBT_2 1.000 0.000 999.000 999.000
##
## WPUBT_3 BY
## PUBT_3 1.000 0.000 999.000 999.000
##
## WWCHRT_1 ON
## WWCHRT_B -0.155 0.238 -0.652 0.515
## WINTT_B -0.056 0.029 -1.889 0.059
## WPUBT_B 0.036 0.034 1.034 0.301
##
## WWCHRT_2 ON
## WWCHRT_1 0.386 0.071 5.420 0.000
## WINTT_1 -0.021 0.027 -0.780 0.436
## WPUBT_1 -0.016 0.054 -0.293 0.769
##
## WWCHRT_3 ON
## WWCHRT_2 0.425 0.151 2.815 0.005
## WINTT_2 -0.038 0.023 -1.621 0.105
## WPUBT_2 -0.145 0.058 -2.508 0.012
##
## WPUBT_1 ON
## WPUBT_B 0.137 0.065 2.104 0.035
## WINTT_B -0.044 0.041 -1.069 0.285
## WWCHRT_B 0.011 0.253 0.044 0.965
##
## WPUBT_2 ON
## WPUBT_1 0.231 0.066 3.505 0.000
## WINTT_1 0.002 0.026 0.065 0.948
## WWCHRT_1 -0.118 0.081 -1.453 0.146
##
## WPUBT_3 ON
## WPUBT_2 0.190 0.050 3.809 0.000
## WINTT_2 -0.021 0.019 -1.074 0.283
## WWCHRT_2 -0.028 0.048 -0.575 0.566
##
## WINTT_1 ON
## WINTT_B 0.261 0.080 3.255 0.001
## WPUBT_B 0.067 0.070 0.949 0.343
## WWCHRT_B -0.407 0.330 -1.232 0.218
##
## WINTT_2 ON
## WINTT_1 0.262 0.091 2.882 0.004
## WPUBT_1 0.007 0.139 0.052 0.959
## WWCHRT_1 -0.116 0.143 -0.812 0.417
##
## WINTT_3 ON
## WINTT_2 0.450 0.074 6.052 0.000
## WPUBT_2 0.035 0.089 0.394 0.694
## WWCHRT_2 0.175 0.125 1.405 0.160
##
## RI_INTT WITH
## RI_PUBTT 0.028 0.029 0.945 0.345
## RI_WCHRT 0.082 0.041 2.008 0.045
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## RI_PUBTT WITH
## RI_WCHRT 0.036 0.013 2.769 0.006
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## WINTT_B WITH
## WPUBT_B -0.007 0.038 -0.188 0.850
## WWCHRT_B -0.067 0.037 -1.825 0.068
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WWCHRT_B WITH
## WPUBT_B 0.002 0.016 0.108 0.914
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WINTT_1 WITH
## WPUBT_1 -0.021 0.052 -0.403 0.687
## WWCHRT_1 -0.041 0.040 -1.029 0.303
##
## WPUBT_1 WITH
## WWCHRT_1 0.002 0.029 0.071 0.943
##
## WINTT_2 WITH
## WPUBT_2 0.025 0.033 0.749 0.454
## WWCHRT_2 -0.001 0.032 -0.039 0.969
##
## WPUBT_2 WITH
## WWCHRT_2 -0.033 0.015 -2.288 0.022
##
## WINTT_3 WITH
## WPUBT_3 0.023 0.033 0.680 0.496
## WWCHRT_3 0.065 0.060 1.090 0.276
##
## WPUBT_3 WITH
## WWCHRT_3 0.003 0.021 0.149 0.882
##
## RI_WCHRT WITH
## WPUBT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## WCHRT_B 0.119 0.019 6.268 0.000
## WCHRT_1 0.144 0.020 7.252 0.000
## WCHRT_2 0.242 0.025 9.746 0.000
## WCHRT_3 0.135 0.036 3.799 0.000
## INTT_B 0.057 0.043 1.323 0.186
## INTT_1 0.015 0.044 0.338 0.735
## INTT_2 -0.078 0.048 -1.644 0.100
## INTT_3 -0.165 0.055 -3.018 0.003
## PUBT_B 0.321 0.021 15.004 0.000
## PUBT_1 0.358 0.031 11.401 0.000
## PUBT_2 0.280 0.020 14.305 0.000
## PUBT_3 0.171 0.020 8.611 0.000
##
## Variances
## RI_INTT 1.690 0.186 9.094 0.000
## RI_PUBTT 0.116 0.019 6.080 0.000
## RI_WCHRT 0.549 0.030 18.589 0.000
## WINTT_B 1.641 0.199 8.238 0.000
## WWCHRT_B 0.099 0.024 4.093 0.000
## WPUBT_B 0.538 0.027 19.719 0.000
##
## Residual Variances
## WCHRT_B 0.000 0.000 999.000 999.000
## WCHRT_1 0.000 0.000 999.000 999.000
## WCHRT_2 0.000 0.000 999.000 999.000
## WCHRT_3 0.000 0.000 999.000 999.000
## INTT_B 0.000 0.000 999.000 999.000
## INTT_1 0.000 0.000 999.000 999.000
## INTT_2 0.000 0.000 999.000 999.000
## INTT_3 0.000 0.000 999.000 999.000
## PUBT_B 0.000 0.000 999.000 999.000
## PUBT_1 0.000 0.000 999.000 999.000
## PUBT_2 0.000 0.000 999.000 999.000
## PUBT_3 0.000 0.000 999.000 999.000
## WINTT_1 1.368 0.159 8.580 0.000
## WINTT_2 1.861 0.217 8.557 0.000
## WINTT_3 2.190 0.189 11.589 0.000
## WWCHRT_1 0.114 0.030 3.763 0.000
## WWCHRT_2 0.333 0.076 4.402 0.000
## WWCHRT_3 0.237 0.034 6.952 0.000
## WPUBT_1 0.439 0.037 11.755 0.000
## WPUBT_2 0.410 0.024 17.299 0.000
## WPUBT_3 0.369 0.024 15.638 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.216E-03
## (ratio of smallest to largest eigenvalue)
##
##
## STANDARDIZED MODEL RESULTS
##
##
## STDYX Standardization
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_INTT BY
## INTT_B 0.712 0.031 22.713 0.000
## INTT_1 0.726 0.035 20.468 0.000
## INTT_2 0.680 0.034 20.054 0.000
## INTT_3 0.628 0.031 20.497 0.000
##
## RI_PUBTT BY
## PUBT_B 0.421 0.034 12.399 0.000
## PUBT_1 0.451 0.040 11.300 0.000
## PUBT_2 0.458 0.038 12.041 0.000
## PUBT_3 0.480 0.038 12.637 0.000
##
## RI_WCHRT BY
## WCHRT_B 0.921 0.020 47.178 0.000
## WCHRT_1 0.905 0.019 47.031 0.000
## WCHRT_2 0.780 0.035 22.459 0.000
## WCHRT_3 0.795 0.018 44.590 0.000
##
## WINTT_B BY
## INTT_B 0.702 0.032 22.055 0.000
##
## WINTT_1 BY
## INTT_1 0.687 0.038 18.319 0.000
##
## WINTT_2 BY
## INTT_2 0.734 0.031 23.377 0.000
##
## WINTT_3 BY
## INTT_3 0.778 0.025 31.490 0.000
##
## WWCHRT_B BY
## WCHRT_B 0.390 0.046 8.481 0.000
##
## WWCHRT_1 BY
## WCHRT_1 0.425 0.041 10.367 0.000
##
## WWCHRT_2 BY
## WCHRT_2 0.625 0.043 14.434 0.000
##
## WWCHRT_3 BY
## WCHRT_3 0.606 0.023 25.892 0.000
##
## WPUBT_B BY
## PUBT_B 0.907 0.016 57.719 0.000
##
## WPUBT_1 BY
## PUBT_1 0.893 0.020 44.265 0.000
##
## WPUBT_2 BY
## PUBT_2 0.889 0.020 45.425 0.000
##
## WPUBT_3 BY
## PUBT_3 0.877 0.021 42.228 0.000
##
## WWCHRT_1 ON
## WWCHRT_B -0.140 0.212 -0.659 0.510
## WINTT_B -0.205 0.111 -1.846 0.065
## WPUBT_B 0.075 0.073 1.032 0.302
##
## WWCHRT_2 ON
## WWCHRT_1 0.226 0.059 3.856 0.000
## WINTT_1 -0.044 0.054 -0.817 0.414
## WPUBT_1 -0.018 0.061 -0.295 0.768
##
## WWCHRT_3 ON
## WWCHRT_2 0.447 0.143 3.116 0.002
## WINTT_2 -0.093 0.056 -1.656 0.098
## WPUBT_2 -0.169 0.067 -2.529 0.011
##
## WPUBT_1 ON
## WPUBT_B 0.149 0.069 2.159 0.031
## WINTT_B -0.084 0.077 -1.089 0.276
## WWCHRT_B 0.005 0.118 0.044 0.965
##
## WPUBT_2 ON
## WPUBT_1 0.235 0.068 3.472 0.001
## WINTT_1 0.003 0.049 0.065 0.948
## WWCHRT_1 -0.062 0.041 -1.500 0.134
##
## WPUBT_3 ON
## WPUBT_2 0.202 0.053 3.805 0.000
## WINTT_2 -0.047 0.043 -1.087 0.277
## WWCHRT_2 -0.027 0.046 -0.576 0.565
##
## WINTT_1 ON
## WINTT_B 0.272 0.077 3.537 0.000
## WPUBT_B 0.040 0.042 0.943 0.346
## WWCHRT_B -0.104 0.084 -1.244 0.214
##
## WINTT_2 ON
## WINTT_1 0.230 0.079 2.916 0.004
## WPUBT_1 0.003 0.067 0.052 0.959
## WWCHRT_1 -0.029 0.036 -0.802 0.423
##
## WINTT_3 ON
## WINTT_2 0.392 0.069 5.689 0.000
## WPUBT_2 0.014 0.037 0.395 0.693
## WWCHRT_2 0.065 0.045 1.433 0.152
##
## RI_INTT WITH
## RI_PUBTT 0.063 0.067 0.931 0.352
## RI_WCHRT 0.085 0.042 2.017 0.044
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## RI_PUBTT WITH
## RI_WCHRT 0.145 0.054 2.693 0.007
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## WINTT_B WITH
## WPUBT_B -0.008 0.040 -0.189 0.850
## WWCHRT_B -0.167 0.093 -1.795 0.073
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WWCHRT_B WITH
## WPUBT_B 0.007 0.069 0.108 0.914
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WINTT_1 WITH
## WPUBT_1 -0.027 0.067 -0.403 0.687
## WWCHRT_1 -0.103 0.104 -0.993 0.321
##
## WPUBT_1 WITH
## WWCHRT_1 0.009 0.128 0.071 0.943
##
## WINTT_2 WITH
## WPUBT_2 0.028 0.038 0.746 0.456
## WWCHRT_2 -0.002 0.040 -0.039 0.969
##
## WPUBT_2 WITH
## WWCHRT_2 -0.090 0.034 -2.698 0.007
##
## WINTT_3 WITH
## WPUBT_3 0.025 0.037 0.680 0.496
## WWCHRT_3 0.090 0.080 1.124 0.261
##
## WPUBT_3 WITH
## WWCHRT_3 0.011 0.071 0.148 0.882
##
## RI_WCHRT WITH
## WPUBT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## WCHRT_B 0.148 0.022 6.641 0.000
## WCHRT_1 0.176 0.023 7.814 0.000
## WCHRT_2 0.255 0.022 11.555 0.000
## WCHRT_3 0.145 0.037 3.888 0.000
## INTT_B 0.031 0.023 1.377 0.169
## INTT_1 0.008 0.024 0.341 0.733
## INTT_2 -0.041 0.026 -1.559 0.119
## INTT_3 -0.079 0.029 -2.775 0.006
## PUBT_B 0.397 0.028 14.215 0.000
## PUBT_1 0.475 0.048 9.892 0.000
## PUBT_2 0.377 0.029 12.946 0.000
## PUBT_3 0.242 0.031 7.932 0.000
##
## Variances
## RI_INTT 1.000 0.000 999.000 999.000
## RI_PUBTT 1.000 0.000 999.000 999.000
## RI_WCHRT 1.000 0.000 999.000 999.000
## WINTT_B 1.000 0.000 999.000 999.000
## WWCHRT_B 1.000 0.000 999.000 999.000
## WPUBT_B 1.000 0.000 999.000 999.000
##
## Residual Variances
## WCHRT_B 0.000 999.000 999.000 999.000
## WCHRT_1 0.000 999.000 999.000 999.000
## WCHRT_2 0.000 999.000 999.000 999.000
## WCHRT_3 0.000 999.000 999.000 999.000
## INTT_B 0.000 999.000 999.000 999.000
## INTT_1 0.000 999.000 999.000 999.000
## INTT_2 0.000 999.000 999.000 999.000
## INTT_3 0.000 999.000 999.000 999.000
## PUBT_B 0.000 999.000 999.000 999.000
## PUBT_1 0.000 999.000 999.000 999.000
## PUBT_2 0.000 999.000 999.000 999.000
## PUBT_3 0.000 999.000 999.000 999.000
## WINTT_1 0.904 0.044 20.398 0.000
## WINTT_2 0.945 0.037 25.666 0.000
## WINTT_3 0.843 0.053 15.993 0.000
## WWCHRT_1 0.942 0.072 13.135 0.000
## WWCHRT_2 0.945 0.025 37.245 0.000
## WWCHRT_3 0.744 0.127 5.850 0.000
## WPUBT_1 0.970 0.024 39.773 0.000
## WPUBT_2 0.942 0.032 29.554 0.000
## WPUBT_3 0.956 0.022 44.143 0.000
##
##
## R-SQUARE
##
## Observed Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WCHRT_B 1.000 999.000 999.000 999.000
## WCHRT_1 1.000 999.000 999.000 999.000
## WCHRT_2 1.000 999.000 999.000 999.000
## WCHRT_3 1.000 999.000 999.000 999.000
## INTT_B 1.000 999.000 999.000 999.000
## INTT_1 1.000 999.000 999.000 999.000
## INTT_2 1.000 999.000 999.000 999.000
## INTT_3 1.000 999.000 999.000 999.000
## PUBT_B 1.000 999.000 999.000 999.000
## PUBT_1 1.000 999.000 999.000 999.000
## PUBT_2 1.000 999.000 999.000 999.000
## PUBT_3 1.000 999.000 999.000 999.000
##
## Latent Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WINTT_1 0.096 0.044 2.158 0.031
## WINTT_2 0.055 0.037 1.503 0.133
## WINTT_3 0.157 0.053 2.970 0.003
## WWCHRT_1 0.058 0.072 0.803 0.422
## WWCHRT_2 0.055 0.025 2.188 0.029
## WWCHRT_3 0.256 0.127 2.009 0.045
## WPUBT_1 0.030 0.024 1.213 0.225
## WPUBT_2 0.058 0.032 1.823 0.068
## WPUBT_3 0.044 0.022 2.038 0.042
##
##
## MODEL MODIFICATION INDICES
##
## NOTE: Modification indices for direct effects of observed dependent variables
## regressed on covariates may not be included. To include these, request
## MODINDICES (ALL).
##
## Minimum M.I. value for printing the modification index 10.000
##
## M.I. E.P.C. Std E.P.C. StdYX E.P.C.
##
## WITH Statements
##
## PUBT_2 WITH PUBT_1 11.930 -0.291 -0.291 999.000
##
##
## TECHNICAL 4 OUTPUT
##
##
## ESTIMATES DERIVED FROM THE MODEL
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 1.690
## RI_PUBTT 0.028 0.116
## RI_WCHRT 0.082 0.036 0.549
## WINTT_B 0.000 0.000 0.000 1.641
## WINTT_1 0.000 0.000 0.000 0.456 1.513
## WINTT_2 0.000 0.000 0.000 0.128 0.403
## WINTT_3 0.000 0.000 0.000 0.051 0.172
## WWCHRT_B 0.000 0.000 0.000 -0.067 -0.058
## WWCHRT_1 0.000 0.000 0.000 -0.081 -0.056
## WWCHRT_2 0.000 0.000 0.000 -0.040 -0.053
## WWCHRT_3 0.000 0.000 0.000 -0.021 -0.038
## WPUBT_B 0.000 0.000 0.000 -0.007 0.033
## WPUBT_1 0.000 0.000 0.000 -0.074 -0.037
## WPUBT_2 0.000 0.000 0.000 -0.007 0.001
## WPUBT_3 0.000 0.000 0.000 -0.003 -0.007
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 1.970
## WINTT_3 0.883 2.596
## WWCHRT_B -0.014 -0.007 0.099
## WWCHRT_1 -0.029 -0.005 -0.011 0.121
## WWCHRT_2 -0.021 0.051 -0.003 0.048 0.353
## WWCHRT_3 -0.087 0.051 -0.001 0.023 0.157
## WPUBT_B 0.007 0.005 0.002 0.019 0.006
## WPUBT_1 -0.007 0.000 0.004 0.008 -0.003
## WPUBT_2 0.027 0.020 0.002 -0.012 -0.040
## WPUBT_3 -0.035 0.007 0.001 -0.003 -0.017
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.319
## WPUBT_B 0.000 0.538
## WPUBT_1 -0.016 0.074 0.453
## WPUBT_2 -0.081 0.015 0.103 0.436
## WPUBT_3 -0.015 0.003 0.020 0.083 0.386
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.186
## RI_PUBTT 0.029 0.019
## RI_WCHRT 0.041 0.013 0.030
## WINTT_B 0.000 0.000 0.000 0.199
## WINTT_1 0.000 0.000 0.000 0.162 0.222
## WINTT_2 0.000 0.000 0.000 0.073 0.176
## WINTT_3 0.000 0.000 0.000 0.037 0.097
## WWCHRT_B 0.000 0.000 0.000 0.037 0.038
## WWCHRT_1 0.000 0.000 0.000 0.039 0.043
## WWCHRT_2 0.000 0.000 0.000 0.023 0.052
## WWCHRT_3 0.000 0.000 0.000 0.013 0.025
## WPUBT_B 0.000 0.000 0.000 0.038 0.043
## WPUBT_1 0.000 0.000 0.000 0.065 0.063
## WPUBT_2 0.000 0.000 0.000 0.023 0.044
## WPUBT_3 0.000 0.000 0.000 0.006 0.013
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.270
## WINTT_3 0.234 0.273
## WWCHRT_B 0.013 0.006 0.024
## WWCHRT_1 0.024 0.014 0.020 0.025
## WWCHRT_2 0.042 0.048 0.008 0.015 0.077
## WWCHRT_3 0.049 0.070 0.004 0.010 0.059
## WPUBT_B 0.018 0.008 0.016 0.017 0.009
## WPUBT_1 0.072 0.035 0.025 0.026 0.031
## WPUBT_2 0.042 0.049 0.007 0.011 0.018
## WPUBT_3 0.040 0.041 0.001 0.003 0.018
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.036
## WPUBT_B 0.005 0.027
## WPUBT_1 0.016 0.036 0.042
## WPUBT_2 0.026 0.012 0.034 0.028
## WPUBT_3 0.022 0.003 0.010 0.025 0.027
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 9.094
## RI_PUBTT 0.945 6.080
## RI_WCHRT 2.008 2.769 18.589
## WINTT_B 0.000 0.000 0.000 8.238
## WINTT_1 0.000 0.000 0.000 2.811 6.817
## WINTT_2 0.000 0.000 0.000 1.770 2.295
## WINTT_3 0.000 0.000 0.000 1.353 1.768
## WWCHRT_B 0.000 0.000 0.000 -1.825 -1.527
## WWCHRT_1 0.000 0.000 0.000 -2.064 -1.305
## WWCHRT_2 0.000 0.000 0.000 -1.717 -1.019
## WWCHRT_3 0.000 0.000 0.000 -1.643 -1.502
## WPUBT_B 0.000 0.000 0.000 -0.188 0.770
## WPUBT_1 0.000 0.000 0.000 -1.141 -0.587
## WPUBT_2 0.000 0.000 0.000 -0.300 0.015
## WPUBT_3 0.000 0.000 0.000 -0.473 -0.533
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 7.296
## WINTT_3 3.781 9.518
## WWCHRT_B -1.049 -1.139 4.093
## WWCHRT_1 -1.208 -0.357 -0.578 4.827
## WWCHRT_2 -0.496 1.077 -0.388 3.255 4.574
## WWCHRT_3 -1.772 0.722 -0.274 2.221 2.653
## WPUBT_B 0.387 0.559 0.108 1.151 0.614
## WPUBT_1 -0.102 -0.007 0.172 0.307 -0.106
## WPUBT_2 0.645 0.422 0.328 -1.092 -2.185
## WPUBT_3 -0.884 0.164 0.553 -0.910 -0.954
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 8.739
## WPUBT_B -0.009 19.719
## WPUBT_1 -0.992 2.023 10.658
## WPUBT_2 -3.063 1.267 3.053 15.361
## WPUBT_3 -0.667 0.921 2.001 3.366 14.123
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.345 0.000
## RI_WCHRT 0.045 0.006 0.000
## WINTT_B 1.000 1.000 1.000 0.000
## WINTT_1 1.000 1.000 1.000 0.005 0.000
## WINTT_2 1.000 1.000 1.000 0.077 0.022
## WINTT_3 1.000 1.000 1.000 0.176 0.077
## WWCHRT_B 1.000 1.000 1.000 0.068 0.127
## WWCHRT_1 1.000 1.000 1.000 0.039 0.192
## WWCHRT_2 1.000 1.000 1.000 0.086 0.308
## WWCHRT_3 1.000 1.000 1.000 0.100 0.133
## WPUBT_B 1.000 1.000 1.000 0.850 0.441
## WPUBT_1 1.000 1.000 1.000 0.254 0.557
## WPUBT_2 1.000 1.000 1.000 0.764 0.988
## WPUBT_3 1.000 1.000 1.000 0.636 0.594
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.000 0.000
## WWCHRT_B 0.294 0.255 0.000
## WWCHRT_1 0.227 0.721 0.563 0.000
## WWCHRT_2 0.620 0.281 0.698 0.001 0.000
## WWCHRT_3 0.076 0.471 0.784 0.026 0.008
## WPUBT_B 0.698 0.576 0.914 0.250 0.539
## WPUBT_1 0.918 0.994 0.864 0.758 0.915
## WPUBT_2 0.519 0.673 0.743 0.275 0.029
## WPUBT_3 0.377 0.870 0.581 0.363 0.340
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.992 0.000
## WPUBT_1 0.321 0.043 0.000
## WPUBT_2 0.002 0.205 0.002 0.000
## WPUBT_3 0.505 0.357 0.045 0.001 0.000
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 1.000
## RI_PUBTT 0.063 1.000
## RI_WCHRT 0.085 0.145 1.000
## WINTT_B 0.000 0.000 0.000 1.000
## WINTT_1 0.000 0.000 0.000 0.289 1.000
## WINTT_2 0.000 0.000 0.000 0.071 0.233
## WINTT_3 0.000 0.000 0.000 0.024 0.087
## WWCHRT_B 0.000 0.000 0.000 -0.167 -0.149
## WWCHRT_1 0.000 0.000 0.000 -0.182 -0.131
## WWCHRT_2 0.000 0.000 0.000 -0.052 -0.073
## WWCHRT_3 0.000 0.000 0.000 -0.029 -0.054
## WPUBT_B 0.000 0.000 0.000 -0.008 0.037
## WPUBT_1 0.000 0.000 0.000 -0.086 -0.045
## WPUBT_2 0.000 0.000 0.000 -0.008 0.001
## WPUBT_3 0.000 0.000 0.000 -0.004 -0.009
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 1.000
## WINTT_3 0.390 1.000
## WWCHRT_B -0.031 -0.013 1.000
## WWCHRT_1 -0.059 -0.009 -0.105 1.000
## WWCHRT_2 -0.025 0.054 -0.018 0.231 1.000
## WWCHRT_3 -0.109 0.056 -0.007 0.118 0.467
## WPUBT_B 0.007 0.004 0.007 0.075 0.013
## WPUBT_1 -0.008 0.000 0.020 0.035 -0.008
## WPUBT_2 0.029 0.019 0.011 -0.054 -0.102
## WPUBT_3 -0.040 0.007 0.004 -0.014 -0.046
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 1.000
## WPUBT_B 0.000 1.000
## WPUBT_1 -0.042 0.149 1.000
## WPUBT_2 -0.217 0.031 0.233 1.000
## WPUBT_3 -0.042 0.006 0.048 0.203 1.000
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.067 0.000
## RI_WCHRT 0.042 0.054 0.000
## WINTT_B 0.000 0.000 0.000 0.000
## WINTT_1 0.000 0.000 0.000 0.074 0.000
## WINTT_2 0.000 0.000 0.000 0.036 0.079
## WINTT_3 0.000 0.000 0.000 0.017 0.043
## WWCHRT_B 0.000 0.000 0.000 0.093 0.095
## WWCHRT_1 0.000 0.000 0.000 0.087 0.098
## WWCHRT_2 0.000 0.000 0.000 0.028 0.067
## WWCHRT_3 0.000 0.000 0.000 0.017 0.035
## WPUBT_B 0.000 0.000 0.000 0.040 0.048
## WPUBT_1 0.000 0.000 0.000 0.075 0.076
## WPUBT_2 0.000 0.000 0.000 0.027 0.054
## WPUBT_3 0.000 0.000 0.000 0.008 0.016
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.068 0.000
## WWCHRT_B 0.028 0.011 0.000
## WWCHRT_1 0.046 0.025 0.201 0.000
## WWCHRT_2 0.048 0.051 0.048 0.056 0.000
## WWCHRT_3 0.060 0.077 0.025 0.043 0.144
## WPUBT_B 0.018 0.007 0.069 0.066 0.021
## WPUBT_1 0.076 0.032 0.118 0.113 0.077
## WPUBT_2 0.045 0.046 0.033 0.049 0.040
## WPUBT_3 0.045 0.041 0.008 0.015 0.048
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.012 0.000
## WPUBT_1 0.042 0.069 0.000
## WPUBT_2 0.068 0.023 0.067 0.000
## WPUBT_3 0.063 0.006 0.022 0.053 0.000
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 999.000
## RI_PUBTT 0.931 999.000
## RI_WCHRT 2.017 2.693 999.000
## WINTT_B 0.000 0.000 0.000 999.000
## WINTT_1 0.000 0.000 0.000 3.910 999.000
## WINTT_2 0.000 0.000 0.000 2.004 2.948
## WINTT_3 0.000 0.000 0.000 1.448 2.033
## WWCHRT_B 0.000 0.000 0.000 -1.795 -1.575
## WWCHRT_1 0.000 0.000 0.000 -2.080 -1.338
## WWCHRT_2 0.000 0.000 0.000 -1.884 -1.086
## WWCHRT_3 0.000 0.000 0.000 -1.725 -1.559
## WPUBT_B 0.000 0.000 0.000 -0.189 0.764
## WPUBT_1 0.000 0.000 0.000 -1.147 -0.592
## WPUBT_2 0.000 0.000 0.000 -0.300 0.015
## WPUBT_3 0.000 0.000 0.000 -0.475 -0.538
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 999.000
## WINTT_3 5.723 999.000
## WWCHRT_B -1.114 -1.163 999.000
## WWCHRT_1 -1.273 -0.360 -0.522 999.000
## WWCHRT_2 -0.513 1.049 -0.370 4.115 999.000
## WWCHRT_3 -1.808 0.719 -0.267 2.718 3.240
## WPUBT_B 0.389 0.560 0.108 1.151 0.602
## WPUBT_1 -0.102 -0.007 0.172 0.308 -0.106
## WPUBT_2 0.642 0.421 0.324 -1.096 -2.515
## WPUBT_3 -0.884 0.164 0.539 -0.929 -0.965
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 999.000
## WPUBT_B -0.009 999.000
## WPUBT_1 -1.012 2.171 999.000
## WPUBT_2 -3.206 1.315 3.488 999.000
## WPUBT_3 -0.668 0.943 2.168 3.859 999.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.352 0.000
## RI_WCHRT 0.044 0.007 0.000
## WINTT_B 1.000 1.000 1.000 0.000
## WINTT_1 1.000 1.000 1.000 0.000 0.000
## WINTT_2 1.000 1.000 1.000 0.045 0.003
## WINTT_3 1.000 1.000 1.000 0.148 0.042
## WWCHRT_B 1.000 1.000 1.000 0.073 0.115
## WWCHRT_1 1.000 1.000 1.000 0.037 0.181
## WWCHRT_2 1.000 1.000 1.000 0.060 0.277
## WWCHRT_3 1.000 1.000 1.000 0.084 0.119
## WPUBT_B 1.000 1.000 1.000 0.850 0.445
## WPUBT_1 1.000 1.000 1.000 0.251 0.554
## WPUBT_2 1.000 1.000 1.000 0.764 0.988
## WPUBT_3 1.000 1.000 1.000 0.635 0.590
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.000 0.000
## WWCHRT_B 0.265 0.245 0.000
## WWCHRT_1 0.203 0.719 0.602 0.000
## WWCHRT_2 0.608 0.294 0.712 0.000 0.000
## WWCHRT_3 0.071 0.472 0.789 0.007 0.001
## WPUBT_B 0.697 0.576 0.914 0.250 0.547
## WPUBT_1 0.918 0.994 0.864 0.758 0.915
## WPUBT_2 0.521 0.674 0.746 0.273 0.012
## WPUBT_3 0.377 0.870 0.590 0.353 0.334
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.992 0.000
## WPUBT_1 0.311 0.030 0.000
## WPUBT_2 0.001 0.189 0.000 0.000
## WPUBT_3 0.504 0.346 0.030 0.000 0.000
##
##
## DIAGRAM INFORMATION
##
## Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram.
## If running Mplus from the Mplus Diagrammer, the diagram opens automatically.
##
## Diagram output
## c:\users\ocrobert\onedrive - indiana university\abcd\obesity week\mplus\black full model.dgm
##
## Beginning Time: 11:58:49
## Ending Time: 11:58:49
## Elapsed Time: 00:00:00
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2023 Muthen & Muthen
# Specify the path to your Mplus output file
output_path <- "C:/Users/ocrobert/OneDrive - Indiana University/ABCD/Obesity Week/Mplus/white full model.out"
# Read and print the file
output_content <- readLines(output_path)
cat(output_content, sep = "\n")
## Mplus VERSION 8.10
## MUTHEN & MUTHEN
## 07/02/2024 12:01 PM
##
## INPUT INSTRUCTIONS
##
## TITLE: Full Sample Model
## DATA: FILE = "Fullsample_race3_sex_subset.dat";
## VARIABLE:
## NAMES = sex race3 age_B int_B intt_B age_1 int_1 intt_1
## age_2 int_2 intt_2 age_3 int_3 intt_3 wchr_B wchrt_B
## wchr_1 wchrt_1 wchr_2 wchrt_2 wchr_3 wchrt_3 pub_B pubt_B
## pub_1 pubt_1 pub_2 pubt_2 pub_3 pubt_3 int_p_ave famc_ave bw_lbs matage
## matalc_ave matcig_ave matmar_ave ses_lt;
## MISSING=.;
##
## USEVARIABLES ARE
## wchrt_b wchrt_1
## wchrt_2 wchrt_3
## intt_b intt_1
## intt_2 intt_3
## pubt_b pubt_1
## pubt_2 pubt_3;
##
## USEOBSERVATIONS ARE (race3 EQ 1);
##
## DEFINE:
## wchrt_b = wchrt_b*10;
## wchrt_1 = wchrt_1*10;
## wchrt_2 = wchrt_2*10;
## wchrt_3 = wchrt_3*10;
##
## ANALYSIS:
## ESTIMATOR=MLR;
## MODEL=NOCOVARIANCES;
## COVERAGE = 0;
##
## MODEL:
## ! Estimate random inttercept)
## RI_intt BY intt_b@1 intt_1@1 intt_2@1 intt_3@1;
## RI_pubtT BY pubt_b@1 pubt_1@1 pubt_2@1 pubt_3@1;
## RI_wchrt BY wchrt_b@1 wchrt_1@1 wchrt_2@1 wchrt_3@1;
##
## ! RI correlations
## RI_intt with RI_pubtT RI_wchrt;
## RI_pubtT with RI_wchrt;
##
## ! Create within-person centered variables
## wintt_b BY intt_b@1;
## wintt_1 BY intt_1@1;
## wintt_2 BY intt_2@1;
## wintt_3 BY intt_3@1;
##
## wwchrt_b BY wchrt_b@1;
## wwchrt_1 BY wchrt_1@1;
## wwchrt_2 BY wchrt_2@1;
## wwchrt_3 BY wchrt_3@1;
##
## wpubt_b BY pubt_b@1;
## wpubt_1 BY pubt_1@1;
## wpubt_2 BY pubt_2@1;
## wpubt_3 BY pubt_3@1;
##
## ! Constrain the measurement error variances to zero
## intt_b@0;
## intt_1@0;
## intt_2@0;
## intt_3@0;
##
## wchrt_b@0;
## wchrt_1@0;
## wchrt_2@0;
## wchrt_3@0;
##
## pubt_b@0;
## pubt_1@0;
## pubt_2@0;
## pubt_3@0;
##
## ! Estimate the Lagged Effects
## wwchrt_1 ON wwchrt_b;
## wwchrt_1 ON wintt_b;
## wwchrt_1 ON wpubt_b;
##
## wwchrt_2 ON wwchrt_1;
## wwchrt_2 ON wintt_1;
## wwchrt_2 ON wpubt_1;
##
## wwchrt_3 ON wwchrt_2;
## wwchrt_3 ON wintt_2;
## wwchrt_3 ON wpubt_2;
##
## wpubt_1 ON wpubt_b;
## wpubt_1 ON wintt_b;
## wpubt_1 ON wwchrt_b;
##
## wpubt_2 ON wpubt_1;
## wpubt_2 ON wintt_1;
## wpubt_2 ON wwchrt_1;
##
## wpubt_3 ON wpubt_2;
## wpubt_3 ON wintt_2;
## wpubt_3 ON wwchrt_2;
##
## wintt_1 ON wintt_b;
## wintt_1 ON wpubt_b;
## wintt_1 ON wwchrt_b;
##
## wintt_2 ON wintt_1;
## wintt_2 ON wpubt_1;
## wintt_2 ON wwchrt_1;
##
## wintt_3 ON wintt_2;
## wintt_3 ON wpubt_2;
## wintt_3 ON wwchrt_2;
##
## ! Estimate the covariance between the within-person
## ! centered variables at the first wave
## wintt_b with wpubt_b;
## wwchrt_b with wintt_b wpubt_b;
##
## ! Estimate covariances between residuals of within-person components
## ! (i.e., innovations)
## wintt_1 with wpubt_1 wwchrt_1;
## wpubt_1 with wwchrt_1;
##
## wintt_2 with wpubt_2 wwchrt_2;
## wpubt_2 with wwchrt_2;
##
## wintt_3 with wpubt_3 wwchrt_3;
## wpubt_3 with wwchrt_3;
##
## ! ask for variances for all variables that are included;
## [intt_b intt_1 intt_2 intt_3];
## [pubt_b pubt_1 pubt_2 pubt_3];
## [wchrt_b wchrt_1 wchrt_2 wchrt_3];
##
## ! Fix the correlation between the individual factors and the other
## ! exogenous variables to zero (by default these would be estimated)
## RI_wchrt with wwchrt_b@0 wintt_b@0 wpubt_b@0;
## RI_intt with wwchrt_b@0 wintt_b@0 wpubt_b@0;
## RI_pubtT with wwchrt_b@0 wintt_b@0 wpubt_b@0;
##
## OUTPUT: STDYX MODINDICES Tech4;
##
##
##
## *** WARNING
## Data set contains cases with missing on all variables.
## These cases were not included in the analysis.
## Number of cases with missing on all variables: 1
## 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
##
##
##
## Full Sample Model
##
## SUMMARY OF ANALYSIS
##
## Number of groups 1
## Number of observations 6171
##
## Number of dependent variables 12
## Number of independent variables 0
## Number of continuous latent variables 15
##
## Observed dependent variables
##
## Continuous
## WCHRT_B WCHRT_1 WCHRT_2 WCHRT_3 INTT_B INTT_1
## INTT_2 INTT_3 PUBT_B PUBT_1 PUBT_2 PUBT_3
##
## Continuous latent variables
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1 WINTT_2
## WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2 WWCHRT_3 WPUBT_B
## WPUBT_1 WPUBT_2 WPUBT_3
##
##
## Estimator MLR
## Information matrix OBSERVED
## Maximum number of iterations 1000
## Convergence criterion 0.500D-04
## Maximum number of steepest descent iterations 20
## Maximum number of iterations for H1 2000
## Convergence criterion for H1 0.100D-03
##
## Input data file(s)
## Fullsample_race3_sex_subset.dat
##
## Input data format FREE
##
##
## SUMMARY OF DATA
##
## Number of missing data patterns 162
##
##
## COVARIANCE COVERAGE OF DATA
##
## Minimum covariance coverage value 0.000
##
##
## PROPORTION OF DATA PRESENT
##
##
## Covariance Coverage
## WCHRT_B WCHRT_1 WCHRT_2 WCHRT_3 INTT_B
## ________ ________ ________ ________ ________
## WCHRT_B 0.998
## WCHRT_1 0.961 0.963
## WCHRT_2 0.812 0.803 0.813
## WCHRT_3 0.180 0.178 0.166 0.181
## INTT_B 0.998 0.962 0.813 0.180 0.999
## INTT_1 0.966 0.962 0.805 0.179 0.968
## INTT_2 0.943 0.928 0.812 0.179 0.944
## INTT_3 0.887 0.873 0.770 0.180 0.888
## PUBT_B 0.825 0.796 0.682 0.162 0.825
## PUBT_1 0.468 0.465 0.450 0.118 0.468
## PUBT_2 0.910 0.898 0.784 0.171 0.911
## PUBT_3 0.875 0.860 0.759 0.175 0.876
##
##
## Covariance Coverage
## INTT_1 INTT_2 INTT_3 PUBT_B PUBT_1
## ________ ________ ________ ________ ________
## INTT_1 0.968
## INTT_2 0.933 0.945
## INTT_3 0.878 0.877 0.889
## PUBT_B 0.800 0.781 0.739 0.826
## PUBT_1 0.468 0.457 0.447 0.427 0.468
## PUBT_2 0.901 0.907 0.847 0.760 0.446
## PUBT_3 0.865 0.862 0.861 0.732 0.444
##
##
## Covariance Coverage
## PUBT_2 PUBT_3
## ________ ________
## PUBT_2 0.912
## PUBT_3 0.843 0.877
##
##
##
## UNIVARIATE SAMPLE STATISTICS
##
##
## UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
##
## Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
## Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
##
## WCHRT_B -0.146 1.105 -1.812 0.02% -0.603 -0.366 -0.252
## 6160.000 0.340 2.034 3.298 0.02% -0.123 0.256
## WCHRT_1 -0.132 2.805 -2.125 0.02% -0.620 -0.386 -0.261
## 5941.000 0.447 26.088 9.378 0.02% -0.126 0.290
## WCHRT_2 -0.139 1.519 -2.197 0.02% -0.667 -0.413 -0.279
## 5017.000 0.453 8.055 9.026 0.02% -0.131 0.350
## WCHRT_3 -0.153 2.126 -1.493 0.09% -0.707 -0.448 -0.301
## 1114.000 0.523 13.217 7.699 0.09% -0.173 0.343
## INTT_B -0.064 2.525 -1.161 0.26% -1.096 -0.936 -0.917
## 6167.000 2.584 8.608 12.919 0.02% -0.143 0.882
## INTT_1 -0.047 2.446 -1.281 0.03% -1.132 -1.059 -0.993
## 5974.000 2.962 7.519 12.912 0.02% -0.154 0.883
## INTT_2 -0.026 2.345 -1.697 0.02% -1.281 -1.119 -1.030
## 5829.000 3.659 6.956 13.867 0.02% -0.293 0.861
## INTT_3 0.012 2.098 -1.822 0.04% -1.453 -1.296 -0.630
## 5484.000 4.589 5.261 13.693 0.02% -0.394 1.397
## PUBT_B -0.108 0.148 -1.606 0.14% -0.943 -0.151 0.045
## 5096.000 0.597 -0.731 3.039 0.02% 0.061 0.622
## PUBT_1 -0.097 -0.002 -2.124 0.07% -0.903 -0.084 -0.009
## 2888.000 0.602 -0.378 3.082 0.03% 0.112 0.552
## PUBT_2 -0.128 -0.269 -2.839 0.02% -0.768 -0.266 -0.112
## 5627.000 0.557 0.055 2.854 0.02% 0.079 0.531
## PUBT_3 -0.088 -0.381 -2.940 0.02% -0.693 -0.238 0.032
## 5409.000 0.545 0.286 2.154 0.02% 0.170 0.496
##
##
## THE MODEL ESTIMATION TERMINATED NORMALLY
##
##
##
## MODEL FIT INFORMATION
##
## Number of Free Parameters 69
##
## Loglikelihood
##
## H0 Value -76379.100
## H0 Scaling Correction Factor 2.6729
## for MLR
## H1 Value -76301.931
## H1 Scaling Correction Factor 2.3340
## for MLR
##
## Information Criteria
##
## Akaike (AIC) 152896.200
## Bayesian (BIC) 153360.406
## Sample-Size Adjusted BIC 153141.142
## (n* = (n + 2) / 24)
##
## Chi-Square Test of Model Fit
##
## Value 126.438*
## Degrees of Freedom 21
## P-Value 0.0000
## Scaling Correction Factor 1.2207
## for MLR
##
## * The chi-square value for MLM, MLMV, MLR, ULSMV, WLSM and WLSMV cannot be used
## for chi-square difference testing in the regular way. MLM, MLR and WLSM
## chi-square difference testing is described on the Mplus website. MLMV, WLSMV,
## and ULSMV difference testing is done using the DIFFTEST option.
##
## RMSEA (Root Mean Square Error Of Approximation)
##
## Estimate 0.029
## 90 Percent C.I. 0.024 0.033
## Probability RMSEA <= .05 1.000
##
## CFI/TLI
##
## CFI 0.989
## TLI 0.965
##
## Chi-Square Test of Model Fit for the Baseline Model
##
## Value 9451.286
## Degrees of Freedom 66
## P-Value 0.0000
##
## SRMR (Standardized Root Mean Square Residual)
##
## Value 0.016
##
##
##
## MODEL RESULTS
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_INTT BY
## INTT_B 1.000 0.000 999.000 999.000
## INTT_1 1.000 0.000 999.000 999.000
## INTT_2 1.000 0.000 999.000 999.000
## INTT_3 1.000 0.000 999.000 999.000
##
## RI_PUBTT BY
## PUBT_B 1.000 0.000 999.000 999.000
## PUBT_1 1.000 0.000 999.000 999.000
## PUBT_2 1.000 0.000 999.000 999.000
## PUBT_3 1.000 0.000 999.000 999.000
##
## RI_WCHRT BY
## WCHRT_B 1.000 0.000 999.000 999.000
## WCHRT_1 1.000 0.000 999.000 999.000
## WCHRT_2 1.000 0.000 999.000 999.000
## WCHRT_3 1.000 0.000 999.000 999.000
##
## WINTT_B BY
## INTT_B 1.000 0.000 999.000 999.000
##
## WINTT_1 BY
## INTT_1 1.000 0.000 999.000 999.000
##
## WINTT_2 BY
## INTT_2 1.000 0.000 999.000 999.000
##
## WINTT_3 BY
## INTT_3 1.000 0.000 999.000 999.000
##
## WWCHRT_B BY
## WCHRT_B 1.000 0.000 999.000 999.000
##
## WWCHRT_1 BY
## WCHRT_1 1.000 0.000 999.000 999.000
##
## WWCHRT_2 BY
## WCHRT_2 1.000 0.000 999.000 999.000
##
## WWCHRT_3 BY
## WCHRT_3 1.000 0.000 999.000 999.000
##
## WPUBT_B BY
## PUBT_B 1.000 0.000 999.000 999.000
##
## WPUBT_1 BY
## PUBT_1 1.000 0.000 999.000 999.000
##
## WPUBT_2 BY
## PUBT_2 1.000 0.000 999.000 999.000
##
## WPUBT_3 BY
## PUBT_3 1.000 0.000 999.000 999.000
##
## WWCHRT_1 ON
## WWCHRT_B -0.102 0.080 -1.269 0.204
## WINTT_B -0.032 0.018 -1.715 0.086
## WPUBT_B -0.012 0.016 -0.782 0.434
##
## WWCHRT_2 ON
## WWCHRT_1 0.219 0.045 4.882 0.000
## WINTT_1 0.030 0.011 2.630 0.009
## WPUBT_1 0.022 0.020 1.088 0.276
##
## WWCHRT_3 ON
## WWCHRT_2 0.535 0.108 4.974 0.000
## WINTT_2 0.022 0.012 1.799 0.072
## WPUBT_2 -0.003 0.025 -0.118 0.906
##
## WPUBT_1 ON
## WPUBT_B 0.230 0.029 7.967 0.000
## WINTT_B 0.008 0.027 0.306 0.760
## WWCHRT_B -0.041 0.118 -0.344 0.731
##
## WPUBT_2 ON
## WPUBT_1 0.357 0.024 14.763 0.000
## WINTT_1 -0.006 0.012 -0.484 0.628
## WWCHRT_1 -0.030 0.025 -1.204 0.228
##
## WPUBT_3 ON
## WPUBT_2 0.427 0.019 22.612 0.000
## WINTT_2 0.018 0.008 2.334 0.020
## WWCHRT_2 -0.023 0.022 -1.016 0.310
##
## WINTT_1 ON
## WINTT_B -0.041 0.072 -0.562 0.574
## WPUBT_B -0.024 0.040 -0.603 0.546
## WWCHRT_B 0.014 0.188 0.076 0.939
##
## WINTT_2 ON
## WINTT_1 0.229 0.048 4.747 0.000
## WPUBT_1 0.134 0.050 2.682 0.007
## WWCHRT_1 0.014 0.053 0.258 0.796
##
## WINTT_3 ON
## WINTT_2 0.465 0.032 14.602 0.000
## WPUBT_2 0.116 0.037 3.151 0.002
## WWCHRT_2 0.204 0.069 2.950 0.003
##
## RI_INTT WITH
## RI_PUBTT 0.033 0.016 2.132 0.033
## RI_WCHRT 0.126 0.015 8.453 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## RI_PUBTT WITH
## RI_WCHRT 0.056 0.006 9.810 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## WINTT_B WITH
## WPUBT_B -0.007 0.017 -0.387 0.699
## WWCHRT_B -0.019 0.011 -1.773 0.076
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WWCHRT_B WITH
## WPUBT_B -0.008 0.006 -1.372 0.170
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WINTT_1 WITH
## WPUBT_1 0.005 0.026 0.210 0.834
## WWCHRT_1 -0.002 0.018 -0.119 0.905
##
## WPUBT_1 WITH
## WWCHRT_1 0.001 0.011 0.044 0.965
##
## WINTT_2 WITH
## WPUBT_2 0.016 0.014 1.148 0.251
## WWCHRT_2 0.031 0.011 2.820 0.005
##
## WPUBT_2 WITH
## WWCHRT_2 -0.005 0.005 -1.102 0.271
##
## WINTT_3 WITH
## WPUBT_3 0.051 0.013 3.938 0.000
## WWCHRT_3 0.028 0.027 1.026 0.305
##
## WPUBT_3 WITH
## WWCHRT_3 -0.012 0.009 -1.353 0.176
##
## RI_WCHRT WITH
## WPUBT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## WCHRT_B -0.145 0.007 -19.545 0.000
## WCHRT_1 -0.129 0.009 -14.982 0.000
## WCHRT_2 -0.134 0.009 -14.683 0.000
## WCHRT_3 -0.228 0.015 -15.409 0.000
## INTT_B -0.063 0.020 -3.075 0.002
## INTT_1 -0.037 0.022 -1.677 0.093
## INTT_2 -0.017 0.025 -0.669 0.503
## INTT_3 0.038 0.029 1.315 0.188
## PUBT_B -0.109 0.011 -10.132 0.000
## PUBT_1 -0.085 0.013 -6.358 0.000
## PUBT_2 -0.125 0.010 -12.659 0.000
## PUBT_3 -0.087 0.010 -8.756 0.000
##
## Variances
## RI_INTT 1.796 0.089 20.086 0.000
## RI_PUBTT 0.101 0.011 9.384 0.000
## RI_WCHRT 0.260 0.009 29.043 0.000
## WINTT_B 0.867 0.073 11.925 0.000
## WWCHRT_B 0.081 0.006 13.258 0.000
## WPUBT_B 0.501 0.013 37.525 0.000
##
## Residual Variances
## WCHRT_B 0.000 0.000 999.000 999.000
## WCHRT_1 0.000 0.000 999.000 999.000
## WCHRT_2 0.000 0.000 999.000 999.000
## WCHRT_3 0.000 0.000 999.000 999.000
## INTT_B 0.000 0.000 999.000 999.000
## INTT_1 0.000 0.000 999.000 999.000
## INTT_2 0.000 0.000 999.000 999.000
## INTT_3 0.000 0.000 999.000 999.000
## PUBT_B 0.000 0.000 999.000 999.000
## PUBT_1 0.000 0.000 999.000 999.000
## PUBT_2 0.000 0.000 999.000 999.000
## PUBT_3 0.000 0.000 999.000 999.000
## WINTT_1 1.092 0.096 11.348 0.000
## WINTT_2 1.860 0.085 21.801 0.000
## WINTT_3 2.407 0.100 24.141 0.000
## WWCHRT_1 0.184 0.030 6.075 0.000
## WWCHRT_2 0.189 0.019 9.836 0.000
## WWCHRT_3 0.199 0.036 5.541 0.000
## WPUBT_1 0.462 0.015 31.516 0.000
## WPUBT_2 0.392 0.010 40.924 0.000
## WPUBT_3 0.361 0.009 40.688 0.000
##
##
## QUALITY OF NUMERICAL RESULTS
##
## Condition Number for the Information Matrix 0.272E-03
## (ratio of smallest to largest eigenvalue)
##
##
## STANDARDIZED MODEL RESULTS
##
##
## STDYX Standardization
##
## Two-Tailed
## Estimate S.E. Est./S.E. P-Value
##
## RI_INTT BY
## INTT_B 0.821 0.013 61.885 0.000
## INTT_1 0.788 0.016 48.148 0.000
## INTT_2 0.695 0.012 57.447 0.000
## INTT_3 0.622 0.013 47.914 0.000
##
## RI_PUBTT BY
## PUBT_B 0.410 0.022 18.963 0.000
## PUBT_1 0.414 0.023 17.860 0.000
## PUBT_2 0.427 0.023 18.703 0.000
## PUBT_3 0.431 0.023 18.975 0.000
##
## RI_WCHRT BY
## WCHRT_B 0.874 0.010 90.127 0.000
## WCHRT_1 0.764 0.027 28.501 0.000
## WCHRT_2 0.753 0.018 40.964 0.000
## WCHRT_3 0.709 0.040 17.699 0.000
##
## WINTT_B BY
## INTT_B 0.571 0.019 29.872 0.000
##
## WINTT_1 BY
## INTT_1 0.615 0.021 29.311 0.000
##
## WINTT_2 BY
## INTT_2 0.719 0.012 61.605 0.000
##
## WINTT_3 BY
## INTT_3 0.783 0.010 75.991 0.000
##
## WWCHRT_B BY
## WCHRT_B 0.487 0.017 27.954 0.000
##
## WWCHRT_1 BY
## WCHRT_1 0.645 0.032 20.349 0.000
##
## WWCHRT_2 BY
## WCHRT_2 0.658 0.021 31.320 0.000
##
## WWCHRT_3 BY
## WCHRT_3 0.705 0.040 17.536 0.000
##
## WPUBT_B BY
## PUBT_B 0.912 0.010 93.811 0.000
##
## WPUBT_1 BY
## PUBT_1 0.910 0.011 86.201 0.000
##
## WPUBT_2 BY
## PUBT_2 0.904 0.011 84.012 0.000
##
## WPUBT_3 BY
## PUBT_3 0.903 0.011 83.385 0.000
##
## WWCHRT_1 ON
## WWCHRT_B -0.067 0.052 -1.284 0.199
## WINTT_B -0.068 0.041 -1.678 0.093
## WPUBT_B -0.020 0.025 -0.792 0.428
##
## WWCHRT_2 ON
## WWCHRT_1 0.212 0.038 5.607 0.000
## WINTT_1 0.070 0.027 2.549 0.011
## WPUBT_1 0.034 0.032 1.070 0.284
##
## WWCHRT_3 ON
## WWCHRT_2 0.470 0.057 8.311 0.000
## WINTT_2 0.059 0.036 1.659 0.097
## WPUBT_2 -0.004 0.034 -0.117 0.907
##
## WPUBT_1 ON
## WPUBT_B 0.232 0.028 8.313 0.000
## WINTT_B 0.011 0.036 0.306 0.760
## WWCHRT_B -0.016 0.048 -0.344 0.731
##
## WPUBT_2 ON
## WPUBT_1 0.370 0.025 14.882 0.000
## WINTT_1 -0.009 0.019 -0.485 0.628
## WWCHRT_1 -0.019 0.016 -1.211 0.226
##
## WPUBT_3 ON
## WPUBT_2 0.431 0.018 23.924 0.000
## WINTT_2 0.037 0.016 2.333 0.020
## WWCHRT_2 -0.015 0.015 -1.025 0.306
##
## WINTT_1 ON
## WINTT_B -0.036 0.065 -0.560 0.575
## WPUBT_B -0.016 0.027 -0.602 0.547
## WWCHRT_B 0.004 0.051 0.076 0.939
##
## WINTT_2 ON
## WINTT_1 0.172 0.038 4.480 0.000
## WPUBT_1 0.068 0.025 2.696 0.007
## WWCHRT_1 0.004 0.016 0.259 0.796
##
## WINTT_3 ON
## WINTT_2 0.382 0.025 15.364 0.000
## WPUBT_2 0.046 0.015 3.163 0.002
## WWCHRT_2 0.054 0.018 3.073 0.002
##
## RI_INTT WITH
## RI_PUBTT 0.078 0.036 2.153 0.031
## RI_WCHRT 0.184 0.020 9.156 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## RI_PUBTT WITH
## RI_WCHRT 0.344 0.038 8.963 0.000
## WWCHRT_B 0.000 0.000 999.000 999.000
## WINTT_B 0.000 0.000 999.000 999.000
## WPUBT_B 0.000 0.000 999.000 999.000
##
## WINTT_B WITH
## WPUBT_B -0.010 0.026 -0.387 0.699
## WWCHRT_B -0.073 0.042 -1.730 0.084
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WWCHRT_B WITH
## WPUBT_B -0.042 0.031 -1.368 0.171
## RI_WCHRT 0.000 0.000 999.000 999.000
##
## WINTT_1 WITH
## WPUBT_1 0.008 0.036 0.210 0.834
## WWCHRT_1 -0.005 0.039 -0.119 0.905
##
## WPUBT_1 WITH
## WWCHRT_1 0.002 0.039 0.044 0.965
##
## WINTT_2 WITH
## WPUBT_2 0.019 0.016 1.153 0.249
## WWCHRT_2 0.051 0.019 2.782 0.005
##
## WPUBT_2 WITH
## WWCHRT_2 -0.019 0.017 -1.109 0.267
##
## WINTT_3 WITH
## WPUBT_3 0.055 0.014 3.969 0.000
## WWCHRT_3 0.040 0.040 0.987 0.324
##
## WPUBT_3 WITH
## WWCHRT_3 -0.044 0.034 -1.323 0.186
##
## RI_WCHRT WITH
## WPUBT_B 0.000 0.000 999.000 999.000
##
## Intercepts
## WCHRT_B -0.249 0.015 -16.888 0.000
## WCHRT_1 -0.194 0.017 -11.173 0.000
## WCHRT_2 -0.198 0.016 -12.322 0.000
## WCHRT_3 -0.317 0.031 -10.100 0.000
## INTT_B -0.039 0.013 -2.926 0.003
## INTT_1 -0.022 0.013 -1.636 0.102
## INTT_2 -0.009 0.013 -0.663 0.507
## INTT_3 0.018 0.013 1.338 0.181
## PUBT_B -0.140 0.014 -9.991 0.000
## PUBT_1 -0.111 0.017 -6.332 0.000
## PUBT_2 -0.168 0.013 -12.859 0.000
## PUBT_3 -0.118 0.013 -8.921 0.000
##
## Variances
## RI_INTT 1.000 0.000 999.000 999.000
## RI_PUBTT 1.000 0.000 999.000 999.000
## RI_WCHRT 1.000 0.000 999.000 999.000
## WINTT_B 1.000 0.000 999.000 999.000
## WWCHRT_B 1.000 0.000 999.000 999.000
## WPUBT_B 1.000 0.000 999.000 999.000
##
## Residual Variances
## WCHRT_B 0.000 999.000 999.000 999.000
## WCHRT_1 0.000 999.000 999.000 999.000
## WCHRT_2 0.000 999.000 999.000 999.000
## WCHRT_3 0.000 999.000 999.000 999.000
## INTT_B 0.000 999.000 999.000 999.000
## INTT_1 0.000 999.000 999.000 999.000
## INTT_2 0.000 999.000 999.000 999.000
## INTT_3 0.000 999.000 999.000 999.000
## PUBT_B 0.000 999.000 999.000 999.000
## PUBT_1 0.000 999.000 999.000 999.000
## PUBT_2 0.000 999.000 999.000 999.000
## PUBT_3 0.000 999.000 999.000 999.000
## WINTT_1 0.998 0.005 214.255 0.000
## WINTT_2 0.966 0.014 69.614 0.000
## WINTT_3 0.845 0.020 43.081 0.000
## WWCHRT_1 0.991 0.009 109.704 0.000
## WWCHRT_2 0.949 0.017 56.631 0.000
## WWCHRT_3 0.772 0.051 14.985 0.000
## WPUBT_1 0.945 0.013 73.921 0.000
## WPUBT_2 0.863 0.018 47.162 0.000
## WPUBT_3 0.811 0.016 52.004 0.000
##
##
## R-SQUARE
##
## Observed Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WCHRT_B 1.000 999.000 999.000 999.000
## WCHRT_1 1.000 999.000 999.000 999.000
## WCHRT_2 1.000 999.000 999.000 999.000
## WCHRT_3 1.000 999.000 999.000 999.000
## INTT_B 1.000 999.000 999.000 999.000
## INTT_1 1.000 999.000 999.000 999.000
## INTT_2 1.000 999.000 999.000 999.000
## INTT_3 1.000 999.000 999.000 999.000
## PUBT_B 1.000 999.000 999.000 999.000
## PUBT_1 1.000 999.000 999.000 999.000
## PUBT_2 1.000 999.000 999.000 999.000
## PUBT_3 1.000 999.000 999.000 999.000
##
## Latent Two-Tailed
## Variable Estimate S.E. Est./S.E. P-Value
##
## WINTT_1 0.002 0.005 0.345 0.730
## WINTT_2 0.034 0.014 2.475 0.013
## WINTT_3 0.155 0.020 7.920 0.000
## WWCHRT_1 0.009 0.009 0.971 0.331
## WWCHRT_2 0.051 0.017 3.033 0.002
## WWCHRT_3 0.228 0.051 4.433 0.000
## WPUBT_1 0.055 0.013 4.281 0.000
## WPUBT_2 0.137 0.018 7.495 0.000
## WPUBT_3 0.189 0.016 12.117 0.000
##
##
## MODEL MODIFICATION INDICES
##
## NOTE: Modification indices for direct effects of observed dependent variables
## regressed on covariates may not be included. To include these, request
## MODINDICES (ALL).
##
## Minimum M.I. value for printing the modification index 10.000
##
## M.I. E.P.C. Std E.P.C. StdYX E.P.C.
##
## BY Statements
##
## RI_INTT BY INTT_B 67.214 -0.325 -0.435 -0.267
## RI_INTT BY INTT_1 59.436 0.251 0.337 0.198
## RI_PUBTT BY PUBT_1 10.715 0.375 0.119 0.155
## WINTT_B BY INTT_3 39.992 -0.231 -0.215 -0.100
## WINTT_1 BY INTT_2 60.744 -0.502 -0.525 -0.272
## WINTT_1 BY INTT_3 62.316 0.238 0.249 0.115
## WINTT_3 BY INTT_B 49.904 -0.105 -0.177 -0.109
## WINTT_3 BY INTT_1 62.045 0.107 0.180 0.106
## WINTT_3 BY INTT_2 63.225 -0.797 -1.346 -0.697
## WWCHRT_B BY WCHRT_3 12.226 -0.298 -0.085 -0.118
## WWCHRT_3 BY WCHRT_B 11.256 -0.140 -0.071 -0.122
## WWCHRT_3 BY WCHRT_2 10.774 -0.551 -0.280 -0.413
## WPUBT_B BY PUBT_3 12.299 -0.074 -0.052 -0.071
## WPUBT_1 BY PUBT_2 20.170 -0.251 -0.175 -0.235
## WPUBT_1 BY PUBT_3 19.290 0.105 0.074 0.100
## WPUBT_3 BY PUBT_B 21.260 -0.158 -0.105 -0.135
## WPUBT_3 BY PUBT_1 22.039 0.115 0.077 0.100
## WPUBT_3 BY PUBT_2 19.920 -0.325 -0.217 -0.291
##
## ON/BY Statements
##
## RI_INTT ON WINTT_B /
## WINTT_B BY RI_INTT 64.800 -0.616 -0.428 -0.428
## RI_INTT ON WINTT_1 /
## WINTT_1 BY RI_INTT 62.087 0.440 0.343 0.343
## RI_PUBTT ON WPUBT_B /
## WPUBT_B BY RI_PUBTT 17.494 -0.256 -0.569 -0.569
## RI_PUBTT ON WPUBT_1 /
## WPUBT_1 BY RI_PUBTT 18.329 0.178 0.392 0.392
## WINTT_B ON RI_INTT /
## RI_INTT BY WINTT_B 67.118 -0.309 -0.445 -0.445
## WINTT_B ON WINTT_3 /
## WINTT_3 BY WINTT_B 51.136 -0.102 -0.185 -0.185
## WINTT_1 ON RI_INTT /
## RI_INTT BY WINTT_1 62.994 0.288 0.369 0.369
## WINTT_1 ON WINTT_3 /
## WINTT_3 BY WINTT_1 61.751 0.110 0.178 0.178
## WINTT_2 ON WINTT_3 /
## WINTT_3 BY WINTT_2 63.270 -0.798 -0.970 -0.970
## WINTT_3 ON WINTT_B /
## WINTT_B BY WINTT_3 39.992 -0.231 -0.128 -0.128
## WINTT_3 ON WINTT_1 /
## WINTT_1 BY WINTT_3 62.316 0.238 0.147 0.147
## WWCHRT_B ON WWCHRT_3 /
## WWCHRT_3 BY WWCHRT_B 11.593 -0.131 -0.235 -0.235
## WWCHRT_2 ON WWCHRT_3 /
## WWCHRT_3 BY WWCHRT_2 10.775 -0.551 -0.627 -0.627
## WWCHRT_3 ON WWCHRT_B /
## WWCHRT_B BY WWCHRT_3 12.226 -0.298 -0.167 -0.167
## WPUBT_B ON WPUBT_3 /
## WPUBT_3 BY WPUBT_B 16.116 -0.170 -0.160 -0.160
## WPUBT_1 ON RI_PUBTT /
## RI_PUBTT BY WPUBT_1 20.826 0.706 0.321 0.321
## WPUBT_1 ON WPUBT_3 /
## WPUBT_3 BY WPUBT_1 22.388 0.132 0.126 0.126
## WPUBT_2 ON WPUBT_3 /
## WPUBT_3 BY WPUBT_2 19.908 -0.325 -0.321 -0.321
## WPUBT_3 ON WPUBT_B /
## WPUBT_B BY WPUBT_3 12.299 -0.074 -0.078 -0.078
## WPUBT_3 ON WPUBT_1 /
## WPUBT_1 BY WPUBT_3 19.290 0.105 0.110 0.110
##
## WITH Statements
##
## WCHRT_3 WITH WCHRT_B 12.462 -0.026 -0.026 999.000
## INTT_2 WITH WCHRT_2 11.752 0.362 0.362 999.000
## INTT_2 WITH INTT_1 60.026 -0.533 -0.533 999.000
## INTT_3 WITH INTT_B 37.300 -0.200 -0.200 999.000
## INTT_3 WITH INTT_1 62.628 0.257 0.257 999.000
## INTT_3 WITH INTT_2 62.692 -1.909 -1.909 999.000
## PUBT_2 WITH PUBT_1 28.894 -0.111 -0.111 999.000
## PUBT_3 WITH PUBT_B 17.764 -0.040 -0.040 999.000
## PUBT_3 WITH PUBT_1 24.132 0.044 0.044 999.000
## PUBT_3 WITH PUBT_2 19.844 -0.117 -0.117 999.000
## WINTT_B WITH RI_INTT 62.504 -0.526 -0.421 -0.421
## WINTT_1 WITH RI_INTT 62.880 0.498 0.356 0.356
## WINTT_3 WITH WINTT_B 39.145 -0.199 -0.138 -0.138
## WINTT_3 WITH WINTT_1 62.370 0.266 0.164 0.164
## WINTT_3 WITH WINTT_2 62.716 -1.910 -0.903 -0.903
## WWCHRT_3 WITH WWCHRT_B 13.033 -0.025 -0.198 -0.198
## WPUBT_B WITH RI_PUBTT 16.917 -0.126 -0.558 -0.558
## WPUBT_1 WITH RI_PUBTT 18.551 0.072 0.332 0.332
## WPUBT_3 WITH WPUBT_B 12.140 -0.037 -0.086 -0.086
## WPUBT_3 WITH WPUBT_1 24.779 0.051 0.126 0.126
## WPUBT_3 WITH WPUBT_2 19.833 -0.117 -0.311 -0.311
##
## Variances/Residual Variances
##
## WCHRT_2 12.950 0.220 0.220 0.480
## INTT_2 63.421 4.114 4.114 1.105
## PUBT_2 20.397 0.277 0.277 0.498
##
##
## TECHNICAL 4 OUTPUT
##
##
## ESTIMATES DERIVED FROM THE MODEL
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## EST./S.E. FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 0.000 0.000 0.000 0.000 0.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED MEANS FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## 1.000 1.000 1.000 1.000 1.000
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 1.796
## RI_PUBTT 0.033 0.101
## RI_WCHRT 0.126 0.056 0.260
## WINTT_B 0.000 0.000 0.000 0.867
## WINTT_1 0.000 0.000 0.000 -0.035 1.094
## WINTT_2 0.000 0.000 0.000 -0.008 0.250
## WINTT_3 0.000 0.000 0.000 -0.004 0.122
## WWCHRT_B 0.000 0.000 0.000 -0.019 0.002
## WWCHRT_1 0.000 0.000 0.000 -0.025 -0.001
## WWCHRT_2 0.000 0.000 0.000 -0.006 0.032
## WWCHRT_3 0.000 0.000 0.000 -0.004 0.023
## WPUBT_B 0.000 0.000 0.000 -0.007 -0.012
## WPUBT_1 0.000 0.000 0.000 0.007 0.002
## WPUBT_2 0.000 0.000 0.000 0.003 -0.006
## WPUBT_3 0.000 0.000 0.000 0.001 0.001
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 1.926
## WINTT_3 0.908 2.849
## WWCHRT_B 0.000 -0.001 0.081
## WWCHRT_1 0.002 0.009 -0.007 0.186
## WWCHRT_2 0.040 0.059 -0.002 0.041 0.199
## WWCHRT_3 0.063 0.079 -0.001 0.022 0.107
## WPUBT_B 0.013 0.011 -0.008 -0.005 0.001
## WPUBT_1 0.066 0.053 -0.005 -0.001 0.011
## WPUBT_2 0.038 0.070 -0.002 -0.006 -0.003
## WPUBT_3 0.049 0.096 -0.001 -0.003 -0.005
##
##
## ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.258
## WPUBT_B 0.001 0.501
## WPUBT_1 0.007 0.115 0.489
## WPUBT_2 -0.002 0.041 0.174 0.455
## WPUBT_3 -0.014 0.018 0.075 0.195 0.445
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.089
## RI_PUBTT 0.016 0.011
## RI_WCHRT 0.015 0.006 0.009
## WINTT_B 0.000 0.000 0.000 0.073
## WINTT_1 0.000 0.000 0.000 0.060 0.093
## WINTT_2 0.000 0.000 0.000 0.014 0.066
## WINTT_3 0.000 0.000 0.000 0.008 0.036
## WWCHRT_B 0.000 0.000 0.000 0.011 0.014
## WWCHRT_1 0.000 0.000 0.000 0.015 0.016
## WWCHRT_2 0.000 0.000 0.000 0.004 0.015
## WWCHRT_3 0.000 0.000 0.000 0.003 0.009
## WPUBT_B 0.000 0.000 0.000 0.017 0.019
## WPUBT_1 0.000 0.000 0.000 0.025 0.027
## WPUBT_2 0.000 0.000 0.000 0.009 0.017
## WPUBT_3 0.000 0.000 0.000 0.004 0.008
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.097
## WINTT_3 0.086 0.134
## WWCHRT_B 0.004 0.002 0.006
## WWCHRT_1 0.011 0.007 0.006 0.030
## WWCHRT_2 0.013 0.016 0.001 0.009 0.020
## WWCHRT_3 0.024 0.030 0.001 0.007 0.026
## WPUBT_B 0.009 0.005 0.006 0.007 0.003
## WPUBT_1 0.028 0.017 0.010 0.011 0.012
## WPUBT_2 0.018 0.021 0.004 0.006 0.007
## WPUBT_3 0.018 0.019 0.002 0.003 0.006
##
##
## S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.058
## WPUBT_B 0.002 0.013
## WPUBT_1 0.008 0.016 0.018
## WPUBT_2 0.012 0.007 0.016 0.015
## WPUBT_3 0.011 0.004 0.009 0.013 0.014
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 20.086
## RI_PUBTT 2.132 9.384
## RI_WCHRT 8.453 9.810 29.043
## WINTT_B 0.000 0.000 0.000 11.925
## WINTT_1 0.000 0.000 0.000 -0.589 11.777
## WINTT_2 0.000 0.000 0.000 -0.532 3.787
## WINTT_3 0.000 0.000 0.000 -0.593 3.382
## WWCHRT_B 0.000 0.000 0.000 -1.773 0.149
## WWCHRT_1 0.000 0.000 0.000 -1.727 -0.067
## WWCHRT_2 0.000 0.000 0.000 -1.478 2.105
## WWCHRT_3 0.000 0.000 0.000 -1.366 2.406
## WPUBT_B 0.000 0.000 0.000 -0.387 -0.618
## WPUBT_1 0.000 0.000 0.000 0.261 0.085
## WPUBT_2 0.000 0.000 0.000 0.372 -0.333
## WPUBT_3 0.000 0.000 0.000 0.369 0.157
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 19.945
## WINTT_3 10.502 21.187
## WWCHRT_B -0.090 -0.339 13.258
## WWCHRT_1 0.193 1.285 -1.257 6.199
## WWCHRT_2 3.095 3.741 -1.214 4.653 9.878
## WWCHRT_3 2.646 2.610 -1.145 3.072 4.139
## WPUBT_B 1.448 2.084 -1.372 -0.687 0.309
## WPUBT_1 2.321 3.105 -0.537 -0.051 0.907
## WPUBT_2 2.087 3.277 -0.481 -0.971 -0.406
## WPUBT_3 2.781 5.058 -0.455 -1.174 -0.884
##
##
## EST./S.E. FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 4.469
## WPUBT_B 0.317 37.525
## WPUBT_1 0.781 7.207 26.432
## WPUBT_2 -0.161 5.541 10.931 31.282
## WPUBT_3 -1.312 4.841 8.330 15.300 31.129
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.033 0.000
## RI_WCHRT 0.000 0.000 0.000
## WINTT_B 1.000 1.000 1.000 0.000
## WINTT_1 1.000 1.000 1.000 0.556 0.000
## WINTT_2 1.000 1.000 1.000 0.595 0.000
## WINTT_3 1.000 1.000 1.000 0.553 0.001
## WWCHRT_B 1.000 1.000 1.000 0.076 0.882
## WWCHRT_1 1.000 1.000 1.000 0.084 0.947
## WWCHRT_2 1.000 1.000 1.000 0.139 0.035
## WWCHRT_3 1.000 1.000 1.000 0.172 0.016
## WPUBT_B 1.000 1.000 1.000 0.699 0.537
## WPUBT_1 1.000 1.000 1.000 0.794 0.932
## WPUBT_2 1.000 1.000 1.000 0.710 0.739
## WPUBT_3 1.000 1.000 1.000 0.712 0.875
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.000 0.000
## WWCHRT_B 0.929 0.734 0.000
## WWCHRT_1 0.847 0.199 0.209 0.000
## WWCHRT_2 0.002 0.000 0.225 0.000 0.000
## WWCHRT_3 0.008 0.009 0.252 0.002 0.000
## WPUBT_B 0.148 0.037 0.170 0.492 0.758
## WPUBT_1 0.020 0.002 0.591 0.959 0.364
## WPUBT_2 0.037 0.001 0.630 0.331 0.685
## WPUBT_3 0.005 0.000 0.649 0.240 0.377
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED COVARIANCE MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.751 0.000
## WPUBT_1 0.435 0.000 0.000
## WPUBT_2 0.872 0.000 0.000 0.000
## WPUBT_3 0.190 0.000 0.000 0.000 0.000
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 1.000
## RI_PUBTT 0.078 1.000
## RI_WCHRT 0.184 0.344 1.000
## WINTT_B 0.000 0.000 0.000 1.000
## WINTT_1 0.000 0.000 0.000 -0.036 1.000
## WINTT_2 0.000 0.000 0.000 -0.006 0.172
## WINTT_3 0.000 0.000 0.000 -0.003 0.069
## WWCHRT_B 0.000 0.000 0.000 -0.073 0.007
## WWCHRT_1 0.000 0.000 0.000 -0.063 -0.002
## WWCHRT_2 0.000 0.000 0.000 -0.016 0.069
## WWCHRT_3 0.000 0.000 0.000 -0.008 0.043
## WPUBT_B 0.000 0.000 0.000 -0.010 -0.016
## WPUBT_1 0.000 0.000 0.000 0.010 0.003
## WPUBT_2 0.000 0.000 0.000 0.005 -0.008
## WPUBT_3 0.000 0.000 0.000 0.002 0.002
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 1.000
## WINTT_3 0.388 1.000
## WWCHRT_B -0.001 -0.001 1.000
## WWCHRT_1 0.004 0.012 -0.061 1.000
## WWCHRT_2 0.064 0.078 -0.013 0.212 1.000
## WWCHRT_3 0.089 0.092 -0.006 0.100 0.474
## WPUBT_B 0.013 0.009 -0.042 -0.017 0.003
## WPUBT_1 0.068 0.045 -0.027 -0.002 0.034
## WPUBT_2 0.041 0.061 -0.009 -0.020 -0.009
## WPUBT_3 0.053 0.085 -0.004 -0.012 -0.017
##
##
## ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 1.000
## WPUBT_B 0.002 1.000
## WPUBT_1 0.019 0.233 1.000
## WPUBT_2 -0.006 0.087 0.370 1.000
## WPUBT_3 -0.042 0.038 0.161 0.433 1.000
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.036 0.000
## RI_WCHRT 0.020 0.038 0.000
## WINTT_B 0.000 0.000 0.000 0.000
## WINTT_1 0.000 0.000 0.000 0.064 0.000
## WINTT_2 0.000 0.000 0.000 0.011 0.039
## WINTT_3 0.000 0.000 0.000 0.005 0.018
## WWCHRT_B 0.000 0.000 0.000 0.042 0.049
## WWCHRT_1 0.000 0.000 0.000 0.038 0.035
## WWCHRT_2 0.000 0.000 0.000 0.011 0.033
## WWCHRT_3 0.000 0.000 0.000 0.006 0.017
## WPUBT_B 0.000 0.000 0.000 0.026 0.026
## WPUBT_1 0.000 0.000 0.000 0.038 0.037
## WPUBT_2 0.000 0.000 0.000 0.014 0.024
## WPUBT_3 0.000 0.000 0.000 0.006 0.011
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.025 0.000
## WWCHRT_B 0.009 0.004 0.000
## WWCHRT_1 0.019 0.009 0.051 0.000
## WWCHRT_2 0.021 0.021 0.012 0.038 0.000
## WWCHRT_3 0.036 0.039 0.006 0.023 0.056
## WPUBT_B 0.009 0.004 0.031 0.024 0.011
## WPUBT_1 0.029 0.014 0.051 0.037 0.038
## WPUBT_2 0.019 0.018 0.019 0.020 0.022
## WPUBT_3 0.019 0.017 0.008 0.010 0.019
##
##
## S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.006 0.000
## WPUBT_1 0.023 0.028 0.000
## WPUBT_2 0.036 0.014 0.025 0.000
## WPUBT_3 0.033 0.007 0.015 0.018 0.000
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 999.000
## RI_PUBTT 2.153 999.000
## RI_WCHRT 9.156 8.963 999.000
## WINTT_B 0.000 0.000 0.000 999.000
## WINTT_1 0.000 0.000 0.000 -0.572 999.000
## WINTT_2 0.000 0.000 0.000 -0.526 4.472
## WINTT_3 0.000 0.000 0.000 -0.586 3.874
## WWCHRT_B 0.000 0.000 0.000 -1.730 0.149
## WWCHRT_1 0.000 0.000 0.000 -1.655 -0.067
## WWCHRT_2 0.000 0.000 0.000 -1.457 2.135
## WWCHRT_3 0.000 0.000 0.000 -1.389 2.550
## WPUBT_B 0.000 0.000 0.000 -0.387 -0.616
## WPUBT_1 0.000 0.000 0.000 0.261 0.085
## WPUBT_2 0.000 0.000 0.000 0.372 -0.333
## WPUBT_3 0.000 0.000 0.000 0.370 0.157
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 999.000
## WINTT_3 15.481 999.000
## WWCHRT_B -0.090 -0.338 999.000
## WWCHRT_1 0.193 1.293 -1.201 999.000
## WWCHRT_2 3.049 3.652 -1.164 5.524 999.000
## WWCHRT_3 2.454 2.339 -1.128 4.252 8.531
## WPUBT_B 1.458 2.114 -1.368 -0.695 0.308
## WPUBT_1 2.352 3.184 -0.536 -0.051 0.901
## WPUBT_2 2.111 3.321 -0.481 -0.976 -0.407
## WPUBT_3 2.815 5.158 -0.455 -1.186 -0.886
##
##
## EST./S.E. FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 999.000
## WPUBT_B 0.320 999.000
## WPUBT_1 0.804 8.455 999.000
## WPUBT_2 -0.160 6.203 14.955 999.000
## WPUBT_3 -1.260 5.323 10.430 24.062 999.000
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## RI_INTT RI_PUBTT RI_WCHRT WINTT_B WINTT_1
## ________ ________ ________ ________ ________
## RI_INTT 0.000
## RI_PUBTT 0.031 0.000
## RI_WCHRT 0.000 0.000 0.000
## WINTT_B 1.000 1.000 1.000 0.000
## WINTT_1 1.000 1.000 1.000 0.567 0.000
## WINTT_2 1.000 1.000 1.000 0.599 0.000
## WINTT_3 1.000 1.000 1.000 0.558 0.000
## WWCHRT_B 1.000 1.000 1.000 0.084 0.882
## WWCHRT_1 1.000 1.000 1.000 0.098 0.947
## WWCHRT_2 1.000 1.000 1.000 0.145 0.033
## WWCHRT_3 1.000 1.000 1.000 0.165 0.011
## WPUBT_B 1.000 1.000 1.000 0.699 0.538
## WPUBT_1 1.000 1.000 1.000 0.794 0.932
## WPUBT_2 1.000 1.000 1.000 0.710 0.739
## WPUBT_3 1.000 1.000 1.000 0.712 0.875
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WINTT_2 WINTT_3 WWCHRT_B WWCHRT_1 WWCHRT_2
## ________ ________ ________ ________ ________
## WINTT_2 0.000
## WINTT_3 0.000 0.000
## WWCHRT_B 0.929 0.735 0.000
## WWCHRT_1 0.847 0.196 0.230 0.000
## WWCHRT_2 0.002 0.000 0.245 0.000 0.000
## WWCHRT_3 0.014 0.019 0.260 0.000 0.000
## WPUBT_B 0.145 0.035 0.171 0.487 0.758
## WPUBT_1 0.019 0.001 0.592 0.959 0.368
## WPUBT_2 0.035 0.001 0.631 0.329 0.684
## WPUBT_3 0.005 0.000 0.649 0.235 0.376
##
##
## TWO-TAILED P-VALUE FOR ESTIMATED CORRELATION MATRIX FOR THE LATENT VARIABLES
## WWCHRT_3 WPUBT_B WPUBT_1 WPUBT_2 WPUBT_3
## ________ ________ ________ ________ ________
## WWCHRT_3 0.000
## WPUBT_B 0.749 0.000
## WPUBT_1 0.421 0.000 0.000
## WPUBT_2 0.873 0.000 0.000 0.000
## WPUBT_3 0.208 0.000 0.000 0.000 0.000
##
##
## DIAGRAM INFORMATION
##
## Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram.
## If running Mplus from the Mplus Diagrammer, the diagram opens automatically.
##
## Diagram output
## c:\users\ocrobert\onedrive - indiana university\abcd\obesity week\mplus\white full model.dgm
##
## Beginning Time: 12:01:54
## Ending Time: 12:01:55
## Elapsed Time: 00:00:01
##
##
##
## MUTHEN & MUTHEN
## 3463 Stoner Ave.
## Los Angeles, CA 90066
##
## Tel: (310) 391-9971
## Fax: (310) 391-8971
## Web: www.StatModel.com
## Support: Support@StatModel.com
##
## Copyright (c) 1998-2023 Muthen & Muthen